From a2973e3d375bb32eb443762208bdcf420171b982 Mon Sep 17 00:00:00 2001 From: ShaharNK Date: Sat, 25 Jun 2022 11:57:53 +0300 Subject: [PATCH 01/11] opened first file --- examples/terran/marine_helper_funcs.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 examples/terran/marine_helper_funcs.py diff --git a/examples/terran/marine_helper_funcs.py b/examples/terran/marine_helper_funcs.py new file mode 100644 index 00000000..e69de29b From 713a3e74f85132e0346fc5bf096c68540ec87f8d Mon Sep 17 00:00:00 2001 From: ShaharNK Date: Sat, 25 Jun 2022 12:00:00 +0300 Subject: [PATCH 02/11] copied marine helper funcs from local repo --- examples/terran/marine_helper_funcs.py | 224 +++++++++++++++++++++++++ 1 file changed, 224 insertions(+) diff --git a/examples/terran/marine_helper_funcs.py b/examples/terran/marine_helper_funcs.py index e69de29b..1525772b 100644 --- a/examples/terran/marine_helper_funcs.py +++ b/examples/terran/marine_helper_funcs.py @@ -0,0 +1,224 @@ +import asyncio +import os +import sys +import json +from collections import OrderedDict + +from sc2 import maps +from sc2.bot_ai import BotAI +from sc2.ids.ability_id import AbilityId +from sc2.ids.buff_id import BuffId +from sc2.ids.unit_typeid import UnitTypeId +from sc2.ids.upgrade_id import UpgradeId +from sc2.main import run_game + +from baselines.common.atari_wrappers import make_atari, wrap_deepmind +import numpy as np +import tensorflow as tf +from tensorflow import keras +from tensorflow.keras import layers +from tensorflow.keras.utils import plot_model + +sys.path.append(os.path.join(os.path.dirname(__file__), "../..")) + +import numpy as np + +from sc2.data import Difficulty, Race, Result +from sc2.player import Bot, Computer +from sc2.position import Point2, Point3 +from sc2.unit import Unit +from sc2.units import Units + +import time +import pandas as pd +import aiofiles +from aiocsv import AsyncReader, AsyncDictReader, AsyncWriter, AsyncDictWriter +import csv + +from tensorflow import keras +import tensorflow as tf + +# https://www.youtube.com/watch?v=1-QtVxce44k&ab_channel=Altercate + + +def calc_value_func(marine_health_list): + """ + my sum of marine health minus the sum of enemy marine's health + """ + return np.sum( + marine_health_list[0: int(len(marine_health_list)/2)] + ) - np.sum( + marine_health_list[int(len(marine_health_list)/2):] + ) + + +def attack_nearest(bot, marine_health_dict): + for marine in bot.units(UnitTypeId.MARINE): + marine.attack( + bot.enemy_units.closest_n_units(bot.start_location, n=1)[0] + ) + marine_health_dict[marine.tag] = marine.health + bot.marine_tags_list.append(marine.tag) + return marine_health_dict + +def attack_nearest_max_allocation(bot, max_allocation=3): + friendly_marines_list = list(bot.units(UnitTypeId.MARINE)) + friendly_marines_num = len(friendly_marines_list) + + # at each step, attack enough enemies that we have at most max_allocation per enemy + #print(f'attacking {int(np.ceil(friendly_marines_num / max_allocation))} enemies with {friendly_marines_num} marines') + enemy_marines_to_attack = bot.enemy_units.closest_n_units( + bot.start_location, + n=int(np.ceil(friendly_marines_num / max_allocation)) + ) + + # split friendly marines for each enemy + if len(friendly_marines_list) == 0 or len(enemy_marines_to_attack) == 0: + return + else: + friendly_marine_groups = np.array_split(friendly_marines_list, len(enemy_marines_to_attack)) + + for group_num, friendly_marine_group in enumerate(friendly_marine_groups): + for marine in friendly_marine_group: + marine.attack( + enemy_marines_to_attack[group_num] + ) + #marine_health_dict[marine.tag] = marine.health + #bot.marine_tags_list.append(marine.tag) + return + #return marine_health_dict + + +def attack_nearest_max_allocation_tf(bot, max_allocation=3, marine_tag_list=None): + if marine_tag_list is None: + raise ValueError("marien tag list should not be None") + + # create the action dict according to the marine tag list with the tag order + # (so that actions will be attached to the right marine) + actions_dict = OrderedDict({tag: None for tag in marine_tag_list }) + + friendly_marines_list = list(bot.units(UnitTypeId.MARINE)) + friendly_marines_num = len(friendly_marines_list) + + # at each step, attack enough enemies that we have at most max_allocation per enemy + #print(f'attacking {int(np.ceil(friendly_marines_num / max_allocation))} enemies with {friendly_marines_num} marines') + enemy_marines_to_attack = bot.enemy_units.closest_n_units( + bot.start_location, + n=int(np.ceil(friendly_marines_num / max_allocation)) + ) + + # split friendly marines for each enemy + if len(friendly_marines_list) == 0 or len(enemy_marines_to_attack) == 0: + return np.array([None]*8) + else: + friendly_marine_groups = np.array_split(friendly_marines_list, len(enemy_marines_to_attack)) + + for group_num, friendly_marine_group in enumerate(friendly_marine_groups): + for marine in friendly_marine_group: + marine.attack( + enemy_marines_to_attack[group_num] + ) + actions_dict[marine.tag] = enemy_marines_to_attack[group_num].tag + + return actions_dict + + +def build_ordered_marine_tag_list(bot : BotAI): + marine_tag_list = [] + marine_friendly_tag_list = [] + marine_enemy_tag_list = [] + + for unit in bot.units.closest_n_units(bot.start_location, n=40): + if unit.type_id == UnitTypeId.MARINE: + marine_tag_list.append(unit.tag) + marine_friendly_tag_list.append(unit.tag) + + for unit in bot.enemy_units.closest_n_units(bot.start_location, n=40): + if unit.type_id == UnitTypeId.MARINE: + marine_tag_list.append(unit.tag) + marine_enemy_tag_list.append(unit.tag) + + return marine_tag_list, marine_friendly_tag_list, marine_enemy_tag_list + + +def observe_health(bot, marine_tag_list): + marine_health_dict = OrderedDict() + + for tag in marine_tag_list: + marine_health_dict[tag] = 0.0 + + are_there_friendly_marines = False + are_there_enemy_marines = False + + for marine in bot.units(UnitTypeId.MARINE): + marine_health_dict[marine.tag] = marine.health + are_there_friendly_marines = True + + #enemy_marine_health_dict = {} + for enemy_marine in bot.enemy_units(UnitTypeId.MARINE): + marine_health_dict[enemy_marine.tag] = enemy_marine.health + are_there_enemy_marines = True + + #marine_health_dict.update(enemy_marine_health_dict) + + return marine_health_dict, are_there_friendly_marines, are_there_enemy_marines + + +def observe_health_ordered(bot, state_map_list): + marine_health_dict = OrderedDict() + + are_there_friendly_marines = False + are_there_enemy_marines = False + + for marine in bot.units(UnitTypeId.MARINE): + marine_health_dict[marine.tag] = marine.health + are_there_friendly_marines = True + + #enemy_marine_health_dict = {} + for enemy_marine in bot.enemy_units(UnitTypeId.MARINE): + marine_health_dict[enemy_marine.tag] = enemy_marine.health + are_there_enemy_marines = True + + #marine_health_dict.update(enemy_marine_health_dict) + + return marine_health_dict, are_there_friendly_marines, are_there_enemy_marines + + +def translate_actions_dict_to_array(marine_friendly_tag_list: list, marine_enemy_tag_list: list, action_dict: OrderedDict): + """ + marine_tag_list has a list of marines tag, such that the first half is friendly marines + ordered by closeness to starting position. The second half is enemy marines ordered + by the same way + action_dict is simply a map between friendly marine tags to enemy marines tag that they are ordered to attack. + + This function we return a 2D numpy array such that the i-th marine attacked the j-th enemy marine will have a + 1 value, otherwise it's all 0s. + """ + action_array = np.ones( + (len(marine_friendly_tag_list), len(marine_enemy_tag_list)) + )*np.nan + + for friendly_tag, enemy_tag in action_dict.items(): + action_array[marine_friendly_tag_list.index(friendly_tag), :] = 0 + action_array[marine_friendly_tag_list.index(friendly_tag), marine_enemy_tag_list.index(enemy_tag)] = 0 + + return action_array + + +def create_q_model(): + # Network defined by the Deepmind paper + inputs = layers.Input(shape=(16, 1, 1,)) + num_friendly_marines = 8 + num_eneymy_marines = 8 + num_actions = num_friendly_marines * num_eneymy_marines + + # Convolutions on the frames on the screen + layer1 = layers.Flatten()(inputs) + layer2 = layers.Dense(64, activation="relu")(layer1) + layer3 = layers.Dense(num_actions, activation="relu")(layer2) + layer4 = layers.Reshape((num_friendly_marines, num_eneymy_marines, 1))(layer3) + action = layers.Softmax(axis=1)(layer4) + + return keras.Model(inputs=inputs, outputs=action) + + From 9a5917a0a99780cb48c9f75ef8992957c6850bcc Mon Sep 17 00:00:00 2001 From: ShaharNK Date: Sat, 25 Jun 2022 12:01:08 +0300 Subject: [PATCH 03/11] copied marine bot from local repo --- examples/terran/marine_match_bot.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 examples/terran/marine_match_bot.py diff --git a/examples/terran/marine_match_bot.py b/examples/terran/marine_match_bot.py new file mode 100644 index 00000000..e69de29b From 2877e3b382c1dd02f3f037555968cece2e5e9e78 Mon Sep 17 00:00:00 2001 From: ShaharNK Date: Sat, 2 Jul 2022 08:45:36 +0300 Subject: [PATCH 04/11] single working marine match --- examples/terran/marine_match_bot.py | 290 ++++++++++++++++++++++++++++ 1 file changed, 290 insertions(+) diff --git a/examples/terran/marine_match_bot.py b/examples/terran/marine_match_bot.py index e69de29b..5165adb1 100644 --- a/examples/terran/marine_match_bot.py +++ b/examples/terran/marine_match_bot.py @@ -0,0 +1,290 @@ +import os +import sys + +sys.path.append(os.path.join(os.path.dirname(__file__), "../..")) + +# https://www.youtube.com/watch?v=1-QtVxce44k&ab_channel=Altercate + +from marine_helper_funcs import * + + +class MarineBot(BotAI): + def __init__(self, max_allocation, result=None, history_dict=None, run_index=None, meta_meta_run_number=None): + super(MarineBot, self).__init__() + self.event_dict = { + 'pre_started' : asyncio.Event(), + 'started': asyncio.Event() + } + + self.max_allocation = max_allocation + self.result = result + self.report_data = pd.DataFrame() + self.history_dict = history_dict + self.run_index = run_index + self.meta_meta_run_number = meta_meta_run_number + + async def on_start(self): + print('on_start has been called!') + + self.start_timer = time.perf_counter() + self.on_step_time_list = [self.start_timer] + + marine_health_dict = {} + self.marine_tags_list = [] + # for marine in self.units(UnitTypeId.MARINE): + # marine.attack( + # self.enemy_units.closest_n_units(self.start_location, n=1)[0] + # ) + # marine_health_dict[marine.tag] = marine.health + # self.marine_tags_list.append(marine.tag) + #marine_health_dict = attack_nearest(self, marine_health_dict) + + #marine_health_dict = attack_nearest_max_allocation(self, marine_health_dict, max_allocation=self.max_allocation) + + self.marine_tag_list, self.marine_friendly_tag_list, self.marine_enemy_tag_list = build_ordered_marine_tag_list( + self) + + attack_nearest_max_allocation(self, max_allocation=self.max_allocation) + + marine_health_dict, are_there_friendly_marines, are_there_enemy_marines = observe_health(self, marine_tag_list=self.marine_tag_list) + + # enemy_marine_health_dict = {} + # self.enemy_marine_tags_list = [] + # for enemy_marine in self.enemy_units(UnitTypeId.MARINE): + # enemy_marine_health_dict[enemy_marine.tag] = enemy_marine.health + # self.enemy_marine_tags_list.append(enemy_marine.tag) + + # self.report_data = pd.DataFrame( + # data=marine_health_dict, + # index=[0] + # ).join( + # pd.DataFrame( + # data=enemy_marine_health_dict, + # index=[0] + # ) + # ) + self.report_data = pd.DataFrame(marine_health_dict, index=[0]) + + if self.meta_meta_run_number is not None: + async with aiofiles.open(f"new_file{self.meta_meta_run_number}.csv", mode="w", encoding="utf-8", newline="") as afp: + writer = AsyncDictWriter(afp, self.report_data.columns, restval="NULL", quoting=csv.QUOTE_ALL) + await writer.writeheader() + # await writer.writerows([ + # {"name": "Sasha", "age": 42}, + # {"name": "Hana"} + # ]) + await writer.writerows(self.report_data.to_dict(orient='records')) + + self.event_dict['started'].set() + # self.client.game_step = 2 + # for overlord in self.units(UnitTypeId.OVERLORD): + # overlord.attack( + # self.enemy_structures.not_flying.random_or(self.enemy_start_locations[0]).position + # ) + + async def on_step(self, iteration): + are_there_friendly_marines = False + are_there_enemy_marines = False + if not self.event_dict['started'].is_set(): + print('waiting for on_start to happen') + await self.event_dict['started'].wait() + curr_time = time.perf_counter() + print('time diff - '+str(curr_time - self.on_step_time_list[-1])) + self.on_step_time_list.append( + curr_time + ) + + marine_health_dict = {} + + # for marine in self.units(UnitTypeId.MARINE): + # marine.attack( + # self.enemy_units.closest_n_units(self.start_location, n=1)[0] + # ) + # marine_health_dict[marine.tag] = marine.health + # are_there_friendly_marines = True + #marine_health_dict = attack_nearest_max_allocation(self, marine_health_dict, max_allocation=self.max_allocation) + + attack_nearest_max_allocation(self, max_allocation=self.max_allocation) + + # for marine in self.units(UnitTypeId.MARINE): + # are_there_friendly_marines = True + # + # enemy_marine_health_dict = {} + # for enemy_marine in self.enemy_units(UnitTypeId.MARINE): + # enemy_marine_health_dict[enemy_marine.tag] = enemy_marine.health + # are_there_enemy_marines = True + # + # marine_health_dict.update(enemy_marine_health_dict) + + marine_health_dict, are_there_friendly_marines, are_there_enemy_marines = observe_health( + self, marine_tag_list=self.marine_tag_list + ) + + self.report_data.loc[curr_time] = marine_health_dict + + self.report_data.fillna(0.0, inplace=True) + + if self.meta_meta_run_number is not None: + async with aiofiles.open(f"new_file{self.meta_meta_run_number}.csv", mode="w", encoding="utf-8", newline="") as afp: + writer = AsyncDictWriter(afp, self.report_data.columns, restval="NULL", quoting=csv.QUOTE_ALL) + await writer.writeheader() + # await writer.writerows([ + # {"name": "Sasha", "age": 42}, + # {"name": "Hana"} + # ]) + await writer.writerows(self.report_data.to_dict(orient='records')) + + # if friendly or enemy marines health is 0, leave + if (not are_there_enemy_marines) or (not are_there_friendly_marines): + if self.history_dict is not None: + self.history_dict[self.run_index]['report_data'] = self.report_data.to_json() + + print(self.report_data) + print(self.report_data.iloc[-1, :].values) + + if self.result is not None: + self.result.append( + calc_value_func( + list( + self.report_data.iloc[-1, :].values + ) + ) + ) + print('results for this round is: '+str(self.result)) + + await self.client.leave() + + # if iteration == 0: + # await self.chat_send("(glhf)") + # + # # Draw creep pixelmap for debugging + # # self.draw_creep_pixelmap() + # + # # If townhall no longer exists: attack move with all units to enemy start location + # if not self.townhalls: + # for unit in self.units.exclude_type({UnitTypeId.EGG, UnitTypeId.LARVA}): + # unit.attack(self.enemy_start_locations[0]) + # return + # + # hatch: Unit = self.townhalls[0] + # + # # Pick a target location + # target: Point2 = self.enemy_structures.not_flying.random_or(self.enemy_start_locations[0]).position + # + # # Give all zerglings an attack command + # for zergling in self.units(UnitTypeId.ZERGLING): + # zergling.attack(target) + # + # # Inject hatchery if queen has more than 25 energy + # for queen in self.units(UnitTypeId.QUEEN): + # if queen.energy >= 25 and not hatch.has_buff(BuffId.QUEENSPAWNLARVATIMER): + # queen(AbilityId.EFFECT_INJECTLARVA, hatch) + # + # # Pull workers out of gas if we have almost enough gas mined, this will stop mining when we reached 100 gas mined + # if self.vespene >= 88 or self.already_pending_upgrade(UpgradeId.ZERGLINGMOVEMENTSPEED) > 0: + # gas_drones: Units = self.workers.filter(lambda w: w.is_carrying_vespene and len(w.orders) < 2) + # drone: Unit + # for drone in gas_drones: + # minerals: Units = self.mineral_field.closer_than(10, hatch) + # if minerals: + # mineral: Unit = minerals.closest_to(drone) + # drone.gather(mineral, queue=True) + # + # # If we have 100 vespene, this will try to research zergling speed once the spawning pool is at 100% completion + # if self.already_pending_upgrade(UpgradeId.ZERGLINGMOVEMENTSPEED + # ) == 0 and self.can_afford(UpgradeId.ZERGLINGMOVEMENTSPEED): + # spawning_pools_ready: Units = self.structures(UnitTypeId.SPAWNINGPOOL).ready + # if spawning_pools_ready: + # self.research(UpgradeId.ZERGLINGMOVEMENTSPEED) + # + # # If we have less than 2 supply left and no overlord is in the queue: train an overlord + # if self.supply_left < 2 and self.already_pending(UnitTypeId.OVERLORD) < 1: + # self.train(UnitTypeId.OVERLORD, 1) + # + # # While we have less than 88 vespene mined: send drones into extractor one frame at a time + # if ( + # self.gas_buildings.ready and self.vespene < 88 + # and self.already_pending_upgrade(UpgradeId.ZERGLINGMOVEMENTSPEED) == 0 + # ): + # extractor: Unit = self.gas_buildings.first + # if extractor.surplus_harvesters < 0: + # self.workers.random.gather(extractor) + # + # # If we have lost of minerals, make a macro hatchery + # if self.minerals > 500: + # for d in range(4, 15): + # pos: Point2 = hatch.position.towards(self.game_info.map_center, d) + # if await self.can_place_single(UnitTypeId.HATCHERY, pos): + # self.workers.random.build(UnitTypeId.HATCHERY, pos) + # break + # + # # While we have less than 16 drones, make more drones + # if self.can_afford(UnitTypeId.DRONE) and self.supply_workers < 16: + # self.train(UnitTypeId.DRONE) + # + # # If our spawningpool is completed, start making zerglings + # if self.structures(UnitTypeId.SPAWNINGPOOL).ready and self.larva and self.can_afford(UnitTypeId.ZERGLING): + # amount_trained: int = self.train(UnitTypeId.ZERGLING, self.larva.amount) + # + # # If we have no extractor, build extractor + # if ( + # self.gas_buildings.amount + self.already_pending(UnitTypeId.EXTRACTOR) == 0 + # and self.can_afford(UnitTypeId.EXTRACTOR) and self.workers + # ): + # drone: Unit = self.workers.random + # target: Unit = self.vespene_geyser.closest_to(drone) + # drone.build_gas(target) + # + # # If we have no spawning pool, try to build spawning pool + # elif self.structures(UnitTypeId.SPAWNINGPOOL).amount + self.already_pending(UnitTypeId.SPAWNINGPOOL) == 0: + # if self.can_afford(UnitTypeId.SPAWNINGPOOL): + # for d in range(4, 15): + # pos: Point2 = hatch.position.towards(self.game_info.map_center, d) + # if await self.can_place_single(UnitTypeId.SPAWNINGPOOL, pos): + # drone: Unit = self.workers.closest_to(pos) + # drone.build(UnitTypeId.SPAWNINGPOOL, pos) + # + # # If we have no queen, try to build a queen if we have a spawning pool compelted + # elif ( + # self.units(UnitTypeId.QUEEN).amount + self.already_pending(UnitTypeId.QUEEN) < self.townhalls.amount + # and self.structures(UnitTypeId.SPAWNINGPOOL).ready + # ): + # if self.can_afford(UnitTypeId.QUEEN): + # self.train(UnitTypeId.QUEEN) + + # def draw_creep_pixelmap(self): + # for (y, x), value in np.ndenumerate(self.state.creep.data_numpy): + # p = Point2((x, y)) + # h2 = self.get_terrain_z_height(p) + # pos = Point3((p.x, p.y, h2)) + # # Red if there is no creep + # color = Point3((255, 0, 0)) + # if value == 1: + # # Green if there is creep + # color = Point3((0, 255, 0)) + # self._client.debug_box2_out(pos, half_vertex_length=0.25, color=color) + + async def on_end(self, game_result: Result): + #self.report_data.to_csv('./marine_focus_fire_data/marine_health_data.csv') + print(f"{self.time_formatted} On end was called") + + +def main(): + run_game( + maps.get("Blistering Sands_marines2"), # maps.get("marines_4x4"), #maps.get("Blistering Sands_marines"), + [Bot(Race.Terran, MarineBot(max_allocation=6)), Computer(Race.Terran, Difficulty.VeryEasy)], + realtime=True, + #save_replay_as="ZvT.SC2Replay", + disable_fog=True + ) + + +if __name__ == "__main__": + #main() + import time + + start = time.time() + print("hello") + main() + end = time.time() + print(end - start) From fba82292beda3bfb9d0e9536ec993ea461430ee0 Mon Sep 17 00:00:00 2001 From: ShaharKatz Date: Sat, 2 Jul 2022 23:02:37 +0300 Subject: [PATCH 05/11] cleaned up commented code --- examples/terran/marine_match_bot.py | 165 ---------------------------- 1 file changed, 165 deletions(-) diff --git a/examples/terran/marine_match_bot.py b/examples/terran/marine_match_bot.py index 5165adb1..192bc1b9 100644 --- a/examples/terran/marine_match_bot.py +++ b/examples/terran/marine_match_bot.py @@ -31,15 +31,6 @@ async def on_start(self): marine_health_dict = {} self.marine_tags_list = [] - # for marine in self.units(UnitTypeId.MARINE): - # marine.attack( - # self.enemy_units.closest_n_units(self.start_location, n=1)[0] - # ) - # marine_health_dict[marine.tag] = marine.health - # self.marine_tags_list.append(marine.tag) - #marine_health_dict = attack_nearest(self, marine_health_dict) - - #marine_health_dict = attack_nearest_max_allocation(self, marine_health_dict, max_allocation=self.max_allocation) self.marine_tag_list, self.marine_friendly_tag_list, self.marine_enemy_tag_list = build_ordered_marine_tag_list( self) @@ -48,39 +39,15 @@ async def on_start(self): marine_health_dict, are_there_friendly_marines, are_there_enemy_marines = observe_health(self, marine_tag_list=self.marine_tag_list) - # enemy_marine_health_dict = {} - # self.enemy_marine_tags_list = [] - # for enemy_marine in self.enemy_units(UnitTypeId.MARINE): - # enemy_marine_health_dict[enemy_marine.tag] = enemy_marine.health - # self.enemy_marine_tags_list.append(enemy_marine.tag) - - # self.report_data = pd.DataFrame( - # data=marine_health_dict, - # index=[0] - # ).join( - # pd.DataFrame( - # data=enemy_marine_health_dict, - # index=[0] - # ) - # ) self.report_data = pd.DataFrame(marine_health_dict, index=[0]) if self.meta_meta_run_number is not None: async with aiofiles.open(f"new_file{self.meta_meta_run_number}.csv", mode="w", encoding="utf-8", newline="") as afp: writer = AsyncDictWriter(afp, self.report_data.columns, restval="NULL", quoting=csv.QUOTE_ALL) await writer.writeheader() - # await writer.writerows([ - # {"name": "Sasha", "age": 42}, - # {"name": "Hana"} - # ]) await writer.writerows(self.report_data.to_dict(orient='records')) self.event_dict['started'].set() - # self.client.game_step = 2 - # for overlord in self.units(UnitTypeId.OVERLORD): - # overlord.attack( - # self.enemy_structures.not_flying.random_or(self.enemy_start_locations[0]).position - # ) async def on_step(self, iteration): are_there_friendly_marines = False @@ -96,26 +63,8 @@ async def on_step(self, iteration): marine_health_dict = {} - # for marine in self.units(UnitTypeId.MARINE): - # marine.attack( - # self.enemy_units.closest_n_units(self.start_location, n=1)[0] - # ) - # marine_health_dict[marine.tag] = marine.health - # are_there_friendly_marines = True - #marine_health_dict = attack_nearest_max_allocation(self, marine_health_dict, max_allocation=self.max_allocation) - attack_nearest_max_allocation(self, max_allocation=self.max_allocation) - # for marine in self.units(UnitTypeId.MARINE): - # are_there_friendly_marines = True - # - # enemy_marine_health_dict = {} - # for enemy_marine in self.enemy_units(UnitTypeId.MARINE): - # enemy_marine_health_dict[enemy_marine.tag] = enemy_marine.health - # are_there_enemy_marines = True - # - # marine_health_dict.update(enemy_marine_health_dict) - marine_health_dict, are_there_friendly_marines, are_there_enemy_marines = observe_health( self, marine_tag_list=self.marine_tag_list ) @@ -128,10 +77,6 @@ async def on_step(self, iteration): async with aiofiles.open(f"new_file{self.meta_meta_run_number}.csv", mode="w", encoding="utf-8", newline="") as afp: writer = AsyncDictWriter(afp, self.report_data.columns, restval="NULL", quoting=csv.QUOTE_ALL) await writer.writeheader() - # await writer.writerows([ - # {"name": "Sasha", "age": 42}, - # {"name": "Hana"} - # ]) await writer.writerows(self.report_data.to_dict(orient='records')) # if friendly or enemy marines health is 0, leave @@ -154,116 +99,6 @@ async def on_step(self, iteration): await self.client.leave() - # if iteration == 0: - # await self.chat_send("(glhf)") - # - # # Draw creep pixelmap for debugging - # # self.draw_creep_pixelmap() - # - # # If townhall no longer exists: attack move with all units to enemy start location - # if not self.townhalls: - # for unit in self.units.exclude_type({UnitTypeId.EGG, UnitTypeId.LARVA}): - # unit.attack(self.enemy_start_locations[0]) - # return - # - # hatch: Unit = self.townhalls[0] - # - # # Pick a target location - # target: Point2 = self.enemy_structures.not_flying.random_or(self.enemy_start_locations[0]).position - # - # # Give all zerglings an attack command - # for zergling in self.units(UnitTypeId.ZERGLING): - # zergling.attack(target) - # - # # Inject hatchery if queen has more than 25 energy - # for queen in self.units(UnitTypeId.QUEEN): - # if queen.energy >= 25 and not hatch.has_buff(BuffId.QUEENSPAWNLARVATIMER): - # queen(AbilityId.EFFECT_INJECTLARVA, hatch) - # - # # Pull workers out of gas if we have almost enough gas mined, this will stop mining when we reached 100 gas mined - # if self.vespene >= 88 or self.already_pending_upgrade(UpgradeId.ZERGLINGMOVEMENTSPEED) > 0: - # gas_drones: Units = self.workers.filter(lambda w: w.is_carrying_vespene and len(w.orders) < 2) - # drone: Unit - # for drone in gas_drones: - # minerals: Units = self.mineral_field.closer_than(10, hatch) - # if minerals: - # mineral: Unit = minerals.closest_to(drone) - # drone.gather(mineral, queue=True) - # - # # If we have 100 vespene, this will try to research zergling speed once the spawning pool is at 100% completion - # if self.already_pending_upgrade(UpgradeId.ZERGLINGMOVEMENTSPEED - # ) == 0 and self.can_afford(UpgradeId.ZERGLINGMOVEMENTSPEED): - # spawning_pools_ready: Units = self.structures(UnitTypeId.SPAWNINGPOOL).ready - # if spawning_pools_ready: - # self.research(UpgradeId.ZERGLINGMOVEMENTSPEED) - # - # # If we have less than 2 supply left and no overlord is in the queue: train an overlord - # if self.supply_left < 2 and self.already_pending(UnitTypeId.OVERLORD) < 1: - # self.train(UnitTypeId.OVERLORD, 1) - # - # # While we have less than 88 vespene mined: send drones into extractor one frame at a time - # if ( - # self.gas_buildings.ready and self.vespene < 88 - # and self.already_pending_upgrade(UpgradeId.ZERGLINGMOVEMENTSPEED) == 0 - # ): - # extractor: Unit = self.gas_buildings.first - # if extractor.surplus_harvesters < 0: - # self.workers.random.gather(extractor) - # - # # If we have lost of minerals, make a macro hatchery - # if self.minerals > 500: - # for d in range(4, 15): - # pos: Point2 = hatch.position.towards(self.game_info.map_center, d) - # if await self.can_place_single(UnitTypeId.HATCHERY, pos): - # self.workers.random.build(UnitTypeId.HATCHERY, pos) - # break - # - # # While we have less than 16 drones, make more drones - # if self.can_afford(UnitTypeId.DRONE) and self.supply_workers < 16: - # self.train(UnitTypeId.DRONE) - # - # # If our spawningpool is completed, start making zerglings - # if self.structures(UnitTypeId.SPAWNINGPOOL).ready and self.larva and self.can_afford(UnitTypeId.ZERGLING): - # amount_trained: int = self.train(UnitTypeId.ZERGLING, self.larva.amount) - # - # # If we have no extractor, build extractor - # if ( - # self.gas_buildings.amount + self.already_pending(UnitTypeId.EXTRACTOR) == 0 - # and self.can_afford(UnitTypeId.EXTRACTOR) and self.workers - # ): - # drone: Unit = self.workers.random - # target: Unit = self.vespene_geyser.closest_to(drone) - # drone.build_gas(target) - # - # # If we have no spawning pool, try to build spawning pool - # elif self.structures(UnitTypeId.SPAWNINGPOOL).amount + self.already_pending(UnitTypeId.SPAWNINGPOOL) == 0: - # if self.can_afford(UnitTypeId.SPAWNINGPOOL): - # for d in range(4, 15): - # pos: Point2 = hatch.position.towards(self.game_info.map_center, d) - # if await self.can_place_single(UnitTypeId.SPAWNINGPOOL, pos): - # drone: Unit = self.workers.closest_to(pos) - # drone.build(UnitTypeId.SPAWNINGPOOL, pos) - # - # # If we have no queen, try to build a queen if we have a spawning pool compelted - # elif ( - # self.units(UnitTypeId.QUEEN).amount + self.already_pending(UnitTypeId.QUEEN) < self.townhalls.amount - # and self.structures(UnitTypeId.SPAWNINGPOOL).ready - # ): - # if self.can_afford(UnitTypeId.QUEEN): - # self.train(UnitTypeId.QUEEN) - - # def draw_creep_pixelmap(self): - # for (y, x), value in np.ndenumerate(self.state.creep.data_numpy): - # p = Point2((x, y)) - # h2 = self.get_terrain_z_height(p) - # pos = Point3((p.x, p.y, h2)) - # # Red if there is no creep - # color = Point3((255, 0, 0)) - # if value == 1: - # # Green if there is creep - # color = Point3((0, 255, 0)) - # self._client.debug_box2_out(pos, half_vertex_length=0.25, color=color) - async def on_end(self, game_result: Result): #self.report_data.to_csv('./marine_focus_fire_data/marine_health_data.csv') print(f"{self.time_formatted} On end was called") From 15553988c6c72e3db63e4c98b660a1efe768bd03 Mon Sep 17 00:00:00 2001 From: ShaharKatz Date: Sun, 17 Jul 2022 08:09:59 +0300 Subject: [PATCH 06/11] upgraded to use the run_multiple_games in marine meta bot --- examples/terran/data/__init__.py | 0 examples/terran/marine_helper_funcs.py | 2 +- examples/terran/marine_match_bot.py | 2 +- examples/terran/marine_meta_bot.py | 195 +++++++++++++++++++++++++ 4 files changed, 197 insertions(+), 2 deletions(-) create mode 100644 examples/terran/data/__init__.py create mode 100644 examples/terran/marine_meta_bot.py diff --git a/examples/terran/data/__init__.py b/examples/terran/data/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/examples/terran/marine_helper_funcs.py b/examples/terran/marine_helper_funcs.py index 1525772b..ad1970e9 100644 --- a/examples/terran/marine_helper_funcs.py +++ b/examples/terran/marine_helper_funcs.py @@ -10,7 +10,7 @@ from sc2.ids.buff_id import BuffId from sc2.ids.unit_typeid import UnitTypeId from sc2.ids.upgrade_id import UpgradeId -from sc2.main import run_game +from sc2.main import run_game, run_multiple_games, GameMatch from baselines.common.atari_wrappers import make_atari, wrap_deepmind import numpy as np diff --git a/examples/terran/marine_match_bot.py b/examples/terran/marine_match_bot.py index 192bc1b9..9df6fbcb 100644 --- a/examples/terran/marine_match_bot.py +++ b/examples/terran/marine_match_bot.py @@ -9,7 +9,7 @@ class MarineBot(BotAI): - def __init__(self, max_allocation, result=None, history_dict=None, run_index=None, meta_meta_run_number=None): + def __init__(self, max_allocation, result: list = None, history_dict=None, run_index=None, meta_meta_run_number=None): super(MarineBot, self).__init__() self.event_dict = { 'pre_started' : asyncio.Event(), diff --git a/examples/terran/marine_meta_bot.py b/examples/terran/marine_meta_bot.py new file mode 100644 index 00000000..0bee9d89 --- /dev/null +++ b/examples/terran/marine_meta_bot.py @@ -0,0 +1,195 @@ +from marine_helper_funcs import * + +from examples.terran.marine_match_bot import MarineBot + +sys.path.append(os.path.join(os.path.dirname(__file__), "../..")) + + +class MarineMetaBot: + + def __init__(self, history_file_name='history_dict', meta_meta_run_number=1): + # with open("history.csv", mode="w", encoding="utf-8", newline="") as afp: + # # TODO: read existing knowledge as if he bot "rose from failure" + # pass + self.history = pd.DataFrame() + self.repeat_num = 20 + self.history_dict = {} + self.meta_meta_run_number = meta_meta_run_number + self.history_file_name = history_file_name + + def run_meta_bot_multiple_games(self): + value = [] + matches_list = [] + + for i, max_allocation in enumerate([1, 2, 3, 4, 8]): + print(f'this is {i} round') + for j in range(0, self.repeat_num): + + print(f'this is {j} repeat') + self.history.loc[i*self.repeat_num+j, 'max_allocation'] = max_allocation + self.history_dict[i*self.repeat_num+j] = {} + self.history_dict[i * self.repeat_num + j]['max_allocation'] = max_allocation + self.history_dict[i * self.repeat_num + j]['arm_run'] = j + + matches_list.append( + GameMatch( + maps.get("Blistering Sands_marines2"), + # maps.get("marines_4x4"), #maps.get("Blistering Sands_marines"), + [ + Bot( + Race.Terran, + MarineBot(max_allocation=max_allocation, result=value, + history_dict=self.history_dict, run_index=i * self.repeat_num + j, + meta_meta_run_number=self.meta_meta_run_number) + ), + Computer(Race.Terran, Difficulty.Easy) + ], + realtime=True, + # save_replay_as="ZvT.SC2Replay", + disable_fog=True + ) + ) + + + + #self.history.loc[i, 'results'] + run_multiple_games(matches_list) + + for i, max_allocation in enumerate([1, 2, 3, 4, 8]): + print(f'this is {i} round') + for j in range(0, self.repeat_num): + print(f'this is {j} repeat') + print('value is: ' + str(value)) + self.history.loc[i * self.repeat_num + j, 'value'] = value[i * self.repeat_num + j] + + print(self.history) + self.history.to_csv(f'./data/arms_results{self.meta_meta_run_number}.csv') + + # for i, max_allocation in enumerate([1, 2, 3, 4, 8]): + # print(f'this is {i} round') + # for j in range(0, self.repeat_num): + # + # print(f'this is {j} repeat') + # self.history.loc[i*self.repeat_num+j, 'max_allocation'] = max_allocation + # self.history_dict[i*self.repeat_num+j] = {} + # self.history_dict[i * self.repeat_num + j]['max_allocation'] = max_allocation + # self.history_dict[i * self.repeat_num + j]['arm_run'] = j + # time.sleep(10) + # run_game( + # maps.get("Blistering Sands_marines2"), + # # maps.get("marines_4x4"), #maps.get("Blistering Sands_marines"), + # [ + # Bot( + # Race.Terran, + # MarineBot(max_allocation=max_allocation, result=value, + # history_dict=self.history_dict, run_index=i*self.repeat_num+j, + # meta_meta_run_number=self.meta_meta_run_number) + # ), + # Computer(Race.Terran, Difficulty.Easy) + # ], + # realtime=True, + # # save_replay_as="ZvT.SC2Replay", + # disable_fog=True + # ) + # print('value is: '+str(value)) + # self.history.loc[i*self.repeat_num+j, 'value'] = value[i*self.repeat_num+j] + + + #self.history.loc[i, 'results'] + print(self.history) + self.history.to_csv(f'./data/arms_results{self.meta_meta_run_number}.csv') + + with open(f'./data/{self.history_file_name}{self.meta_meta_run_number}.json', 'w') as fd: + json.dump(self.history_dict, fd, indent=4) + + def run_meta_bot(self): + value = [] + for i, max_allocation in enumerate([1, 2, 3, 4, 8]): + print(f'this is {i} round') + for j in range(0, self.repeat_num): + print(f'this is {j} repeat') + self.history.loc[i * self.repeat_num + j, 'max_allocation'] = max_allocation + self.history_dict[i * self.repeat_num + j] = {} + self.history_dict[i * self.repeat_num + j]['max_allocation'] = max_allocation + self.history_dict[i * self.repeat_num + j]['arm_run'] = j + time.sleep(10) + run_game( + maps.get("Blistering Sands_marines2"), + # maps.get("marines_4x4"), #maps.get("Blistering Sands_marines"), + [ + Bot( + Race.Terran, + MarineBot(max_allocation=max_allocation, result=value, + history_dict=self.history_dict, run_index=i * self.repeat_num + j, + meta_meta_run_number=self.meta_meta_run_number) + ), + Computer(Race.Terran, Difficulty.Easy) + ], + realtime=True, + # save_replay_as="ZvT.SC2Replay", + disable_fog=True + ) + print('value is: ' + str(value)) + self.history.loc[i * self.repeat_num + j, 'value'] = value[i * self.repeat_num + j] + + # self.history.loc[i, 'results'] + print(self.history) + self.history.to_csv(f'./data/arms_results{self.meta_meta_run_number}.csv') + + with open(f'./data/{self.history_file_name}{self.meta_meta_run_number}.json', 'w') as fd: + json.dump(self.history_dict, fd, indent=4) + + +def main_meta(): + # run_multiple_games( + # matches= [ + # GameMatch( + # maps.get("Blistering Sands_marines2"), + # # maps.get("marines_4x4"), #maps.get("Blistering Sands_marines"), + # [ + # Bot( + # Race.Terran, + # MarineBot(max_allocation=2, result=None, + # history_dict=None, run_index=1, + # meta_meta_run_number=1) + # ), + # Computer(Race.Terran, Difficulty.Easy) + # ], + # realtime=True, + # # save_replay_as="ZvT.SC2Replay", + # disable_fog=True + # + # ), + # GameMatch( + # maps.get("Blistering Sands_marines2"), + # # maps.get("marines_4x4"), #maps.get("Blistering Sands_marines"), + # [ + # Bot( + # Race.Terran, + # MarineBot(max_allocation=2, result=None, + # history_dict=None, run_index=1, + # meta_meta_run_number=1) + # ), + # Computer(Race.Terran, Difficulty.Easy) + # ], + # realtime=True, + # # save_replay_as="ZvT.SC2Replay", + # disable_fog=True + # + # ) + # ] + # ) + + meta_bot = MarineMetaBot(meta_meta_run_number=2) + meta_bot.run_meta_bot_multiple_games() + + +if __name__ == "__main__": + #main() + import time + + start = time.time() + print("hello") + main_meta() + end = time.time() + print(end - start) From e2213f4e1544a3131845eff423bdc3aa494180ed Mon Sep 17 00:00:00 2001 From: ShaharKatz Date: Sun, 31 Jul 2022 23:08:17 +0300 Subject: [PATCH 07/11] copied marine gym to project based on gym environment --- examples/terran/marine_gym.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 examples/terran/marine_gym.py diff --git a/examples/terran/marine_gym.py b/examples/terran/marine_gym.py new file mode 100644 index 00000000..e69de29b From c7aee400089a5cc508141c6b8ddf83844ba7ece4 Mon Sep 17 00:00:00 2001 From: ShaharKatz Date: Sun, 31 Jul 2022 23:31:16 +0300 Subject: [PATCH 08/11] copied the content --- examples/terran/marine_gym.py | 268 ++++++++++++++++++++++++++++++++++ 1 file changed, 268 insertions(+) diff --git a/examples/terran/marine_gym.py b/examples/terran/marine_gym.py index e69de29b..3d3d0695 100644 --- a/examples/terran/marine_gym.py +++ b/examples/terran/marine_gym.py @@ -0,0 +1,268 @@ +import numpy as np +import gym +from gym import spaces + +from enum import Enum +#from stable_baselines import DQN + + +class GoLeftEnv(gym.Env): + """Custom Environment that follows gym interface. + This is a simple env where the agent must learn to go always left. + """ + # Because of google colab, we cannot implement the GUI ('human' render mode) + # metadata = {'render.modes': ['console']} + # Define constants for clearer code + LEFT = 0 + RIGHT = 1 + + def __init__(self, grid_size=10): + super(GoLeftEnv, self).__init__() + # Size of the 1D-grid + self.grid_size = grid_size + + # Initialize the agent at the right of the grid + self.agent_pos = grid_size - 1 + + # Define action and observation space + # They must be gym.spaces objects + # Example when using discrete actions, we have two: left and right + n_actions = 2 + self.action_space = spaces.Discrete(n_actions) + + # The observation will be the coordinate of the agent + # this can be described both by Discrete and Box space + self.observation_space = spaces.Box(low=0, high=self.grid_size, + shape=(1,), dtype=np.float32) + + def reset(self): + """ + Important: the observation must be a numpy array + :return: (np.array) + """ + # Initialize the agent at the right of the grid + self.agent_pos = self.grid_size - 1 + # here we convert to float32 to make it more general (in case we want to use continuous actions) + return np.array([self.agent_pos]).astype(np.float32) + + def step(self, action): + if action == self.LEFT: + self.agent_pos -= 1 + elif action == self.RIGHT: + self.agent_pos += 1 + else: + raise ValueError("Received invalid action={} which is not part of the action space".format(action)) + + # Account for the boundaries of the grid + self.agent_pos = np.clip(self.agent_pos, 0, self.grid_size) + + # Are we at the left of the grid? + done = bool(self.agent_pos == 0) + + # Null reward everywhere except when reaching the goal (left of the grid) + reward = 1 if self.agent_pos == 0 else 0 + + # Optionally we can pass additional info, we are not using that for now + info = {} + + return np.array([self.agent_pos]).astype(np.float32), reward, done, info + + def render(self, mode='console'): + if mode != 'console': + raise NotImplementedError() + # agent is represented as a cross, rest as a dot + print("." * self.agent_pos, end="") + print("x", end="") + print("." * (self.grid_size - self.agent_pos)) + + def close(self): + pass + + +class MarineActionsEnum(Enum): + ATTACK_CLOSEST_TO_ME = 1 + RUN_AWAY = 2 + ATTACK_LOWEST_LIFE_ENEMY = 3 + + +class MockAgent: + def __init__(self): + pass + #self.dqn = DQN() + + @staticmethod + def act(observations): + print(f'got observations - {observations}') + action = MarineActionsEnum.ATTACK_LOWEST_LIFE_ENEMY + print(f'acting {action}') + return action + + +class MockGame: + def __init__(self, starting_obs): + self.observations = starting_obs + + def mock_obs_callback(self, action): + print(f'game got action {action}') + if action == MarineActionsEnum.ATTACK_CLOSEST_TO_ME: + self.observations = np.array( + [np.max([0, marine_health-5]) for marine_health in self.observations] + ).astype(int) + else: + self.observations = np.append( + self.observations[0: int(len(self.observations)/2)], + np.array( + [np.max([0, marine_health-5]) for marine_health in self.observations[int(len(self.observations)/2):]] + ).astype(int) + ) + print(f'game returned observations') + print(f'marine mock health = {self.observations}') + return self.observations + + +def mock_reward_callback(observation_space): + return np.random.rand(1) + + +def reward_health_callback(observation_space): + return np.sum( + observation_space[ 0: int(len(observation_space)/2)] + ) - np.sum( + observation_space[int(len(observation_space)/2):] + ) + + +def calc_value_func(marine_health_list): + """ + my sum of marine health minus the sum of enemy marine's health + """ + return np.sum( + marine_health_list[0: int(len(marine_health_list)/2)] + ) - np.sum( + marine_health_list[int(len(marine_health_list)/2):] + ) + + +class MarineGym(gym.Env): + """Custom Environment that follows gym interface. + This is a simple env where the agent must learn to go always left. + """ + # Because of google colab, we cannot implement the GUI ('human' render mode) + metadata = {'render.modes': ['console']} + + def __init__(self, grid_size=16, + act_callback=MockAgent.act, + reward_callback=mock_reward_callback): + + super(MarineGym, self).__init__() + + # Size of the 1D-grid + self.grid_size = grid_size + + self.act_callback = act_callback + self.reward_callback = reward_callback + + # # Initialize the agent at the right of the grid + self.agent_pos = np.array([45]*self.grid_size).astype(int) + + # Define action and observation space + # They must be gym.spaces objects + # Example when using discrete actions, we have two: left and right + n_actions = len(MarineActionsEnum) + self.action_space = spaces.Discrete(n_actions) + # The observation will be the health of the marines + # this can be described both by Discrete and Box space + self.observation_space = spaces.Box(low=0, high=45, + shape=(grid_size,), dtype=int) + + def reset(self): + """ + Important: the observation must be a numpy array + :return: (np.array) + """ + # # Initialize the agent at the right of the grid + # self.agent_pos = self.grid_size - 1 + # here we convert to float32 to make it more general (in case we want to use continuous actions) + return np.array([45]*self.grid_size).astype(int) + + def update_observations(self, observations): + self.agent_pos = observations + + def step(self, observations): + # self._act(action) + # if action == self.LEFT: + # self.agent_pos -= 1 + # elif action == self.RIGHT: + # self.agent_pos += 1 + # else: + # raise ValueError("Received invalid action={} which is not part of the action space".format(action)) + + self.update_observations(observations) + # # Account for the boundaries of the grid + # self.agent_pos = np.clip(self.agent_pos, 0, self.grid_size) + + action = self.act_callback(observations=self.agent_pos) + + # the agent doesn't decide when it is done + # TODO: not sure this is right + done = False + + reward = self.reward_callback(self.agent_pos) + + # # Are we at the left of the grid? + # done = bool(self.agent_pos == 0) + # + # # Null reward everywhere except when reaching the goal (left of the grid) + # reward = 1 if self.agent_pos == 0 else 0 + + # Optionally we can pass additional info, we are not using that for now + info = {} + + return self.agent_pos, action, reward, done, info + + def render(self, mode='console'): + if mode != 'console': + raise NotImplementedError() + # agent is represented as a cross, rest as a dot + print('render!') + # print("." * self.agent_pos, end="") + # print("x", end="") + # print("." * (self.grid_size - self.agent_pos)) + + def close(self): + pass + + +def test_marine_gym_env(): + env = MarineGym( + grid_size=8, + act_callback=MockAgent.act, + reward_callback=reward_health_callback #mock_reward_callback + ) + + obs = env.reset() + env.render() + + print(env.observation_space) + print(env.action_space) + print(env.action_space.sample()) + + game = MockGame(starting_obs=obs) + + # Hardcoded best agent: always go left! + n_steps = 20 + for step in range(n_steps): + print("Step {}".format(step + 1)) + obs, action, reward, done, info = env.step(observations=game.observations) + print('obs=', obs, 'action=', action, 'reward=', reward, 'done=', done) + env.render() + if done: + print("Goal reached!", "reward=", reward) + break + else: + obs = game.mock_obs_callback(action=action) + env.update_observations(obs) + + +if __name__ == '__main__': + test_marine_gym_env() \ No newline at end of file From caf6e7a7fdf38860e67a17630a9743da096c05ea Mon Sep 17 00:00:00 2001 From: ShaharKatz Date: Tue, 7 Feb 2023 10:28:50 +0200 Subject: [PATCH 09/11] commiting personal code before leaving --- .../{data => marine_faceoff}/__init__.py | 0 .../terran/marine_faceoff/data/__init__.py | 0 .../terran/{ => marine_faceoff}/marine_gym.py | 109 +++++++ .../marine_gym_bot.py} | 0 .../marine_helper_funcs.py | 6 +- .../terran/marine_faceoff/marine_match_bot.py | 125 ++++++++ .../{ => marine_faceoff}/marine_meta_bot.py | 0 .../marine_faceoff/model_orchestrator.py | 25 ++ examples/terran/marine_faceoff/q_models.py | 275 ++++++++++++++++++ 9 files changed, 537 insertions(+), 3 deletions(-) rename examples/terran/{data => marine_faceoff}/__init__.py (100%) create mode 100644 examples/terran/marine_faceoff/data/__init__.py rename examples/terran/{ => marine_faceoff}/marine_gym.py (70%) rename examples/terran/{marine_match_bot.py => marine_faceoff/marine_gym_bot.py} (100%) rename examples/terran/{ => marine_faceoff}/marine_helper_funcs.py (97%) create mode 100644 examples/terran/marine_faceoff/marine_match_bot.py rename examples/terran/{ => marine_faceoff}/marine_meta_bot.py (100%) create mode 100644 examples/terran/marine_faceoff/model_orchestrator.py create mode 100644 examples/terran/marine_faceoff/q_models.py diff --git a/examples/terran/data/__init__.py b/examples/terran/marine_faceoff/__init__.py similarity index 100% rename from examples/terran/data/__init__.py rename to examples/terran/marine_faceoff/__init__.py diff --git a/examples/terran/marine_faceoff/data/__init__.py b/examples/terran/marine_faceoff/data/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/examples/terran/marine_gym.py b/examples/terran/marine_faceoff/marine_gym.py similarity index 70% rename from examples/terran/marine_gym.py rename to examples/terran/marine_faceoff/marine_gym.py index 3d3d0695..2abe0196 100644 --- a/examples/terran/marine_gym.py +++ b/examples/terran/marine_faceoff/marine_gym.py @@ -233,6 +233,97 @@ def close(self): pass +class MarineMetaGym(gym.Env): + """ + Custom Environment that follows gym interface. + This is a simple env where the agent must learn to go always left. + """ + # Because of google colab, we cannot implement the GUI ('human' render mode) + metadata = {'render.modes': ['console']} + + def __init__(self, grid_size=16, + act_callback=MockAgent.act, + reward_callback=mock_reward_callback): + + super(MarineMetaGym, self).__init__() + + # Size of the 1D-grid + self.grid_size = grid_size + + self.act_callback = act_callback + self.reward_callback = reward_callback + + # # Initialize the agent at the right of the grid + self.agent_pos = np.array([45]*self.grid_size).astype(int) + + # Define action and observation space + # They must be gym.spaces objects + # Example when using discrete actions, we have two: left and right + n_actions = len(MarineActionsEnum) + self.action_space = spaces.Discrete(n_actions) + # The observation will be the health of the marines + # this can be described both by Discrete and Box space + self.observation_space = spaces.Box(low=0, high=45, + shape=(grid_size,), dtype=int) + + def reset(self): + """ + Important: the observation must be a numpy array + :return: (np.array) + """ + # # Initialize the agent at the right of the grid + # self.agent_pos = self.grid_size - 1 + # here we convert to float32 to make it more general (in case we want to use continuous actions) + return np.array([45]*self.grid_size).astype(int) + + def update_observations(self, observations): + self.agent_pos = observations + + def step(self, observations): + # self._act(action) + # if action == self.LEFT: + # self.agent_pos -= 1 + # elif action == self.RIGHT: + # self.agent_pos += 1 + # else: + # raise ValueError("Received invalid action={} which is not part of the action space".format(action)) + + self.update_observations(observations) + # # Account for the boundaries of the grid + # self.agent_pos = np.clip(self.agent_pos, 0, self.grid_size) + + action = self.act_callback(observations=self.agent_pos) + + # the agent doesn't decide when it is done + # TODO: not sure this is right + done = False + + reward = self.reward_callback(self.agent_pos) + + # # Are we at the left of the grid? + # done = bool(self.agent_pos == 0) + # + # # Null reward everywhere except when reaching the goal (left of the grid) + # reward = 1 if self.agent_pos == 0 else 0 + + # Optionally we can pass additional info, we are not using that for now + info = {} + + return self.agent_pos, action, reward, done, info + + def render(self, mode='console'): + if mode != 'console': + raise NotImplementedError() + # agent is represented as a cross, rest as a dot + print('render!') + # print("." * self.agent_pos, end="") + # print("x", end="") + # print("." * (self.grid_size - self.agent_pos)) + + def close(self): + pass + + def test_marine_gym_env(): env = MarineGym( grid_size=8, @@ -264,5 +355,23 @@ def test_marine_gym_env(): env.update_observations(obs) +def test_marine_meta_gym_env(): + """ + gym environment using the marine meta bot architecture + """ + env = MarineGym( + grid_size=8, + act_callback=MockAgent.act, + reward_callback=reward_health_callback # mock_reward_callback + ) + + obs = env.reset() + env.render() + + print(env.observation_space) + print(env.action_space) + print(env.action_space.sample()) + + if __name__ == '__main__': test_marine_gym_env() \ No newline at end of file diff --git a/examples/terran/marine_match_bot.py b/examples/terran/marine_faceoff/marine_gym_bot.py similarity index 100% rename from examples/terran/marine_match_bot.py rename to examples/terran/marine_faceoff/marine_gym_bot.py diff --git a/examples/terran/marine_helper_funcs.py b/examples/terran/marine_faceoff/marine_helper_funcs.py similarity index 97% rename from examples/terran/marine_helper_funcs.py rename to examples/terran/marine_faceoff/marine_helper_funcs.py index ad1970e9..a7d06919 100644 --- a/examples/terran/marine_helper_funcs.py +++ b/examples/terran/marine_faceoff/marine_helper_funcs.py @@ -209,14 +209,14 @@ def create_q_model(): # Network defined by the Deepmind paper inputs = layers.Input(shape=(16, 1, 1,)) num_friendly_marines = 8 - num_eneymy_marines = 8 - num_actions = num_friendly_marines * num_eneymy_marines + num_enemy_marines = 8 + num_actions = num_friendly_marines * num_enemy_marines # Convolutions on the frames on the screen layer1 = layers.Flatten()(inputs) layer2 = layers.Dense(64, activation="relu")(layer1) layer3 = layers.Dense(num_actions, activation="relu")(layer2) - layer4 = layers.Reshape((num_friendly_marines, num_eneymy_marines, 1))(layer3) + layer4 = layers.Reshape((num_friendly_marines, num_enemy_marines, 1))(layer3) action = layers.Softmax(axis=1)(layer4) return keras.Model(inputs=inputs, outputs=action) diff --git a/examples/terran/marine_faceoff/marine_match_bot.py b/examples/terran/marine_faceoff/marine_match_bot.py new file mode 100644 index 00000000..9df6fbcb --- /dev/null +++ b/examples/terran/marine_faceoff/marine_match_bot.py @@ -0,0 +1,125 @@ +import os +import sys + +sys.path.append(os.path.join(os.path.dirname(__file__), "../..")) + +# https://www.youtube.com/watch?v=1-QtVxce44k&ab_channel=Altercate + +from marine_helper_funcs import * + + +class MarineBot(BotAI): + def __init__(self, max_allocation, result: list = None, history_dict=None, run_index=None, meta_meta_run_number=None): + super(MarineBot, self).__init__() + self.event_dict = { + 'pre_started' : asyncio.Event(), + 'started': asyncio.Event() + } + + self.max_allocation = max_allocation + self.result = result + self.report_data = pd.DataFrame() + self.history_dict = history_dict + self.run_index = run_index + self.meta_meta_run_number = meta_meta_run_number + + async def on_start(self): + print('on_start has been called!') + + self.start_timer = time.perf_counter() + self.on_step_time_list = [self.start_timer] + + marine_health_dict = {} + self.marine_tags_list = [] + + self.marine_tag_list, self.marine_friendly_tag_list, self.marine_enemy_tag_list = build_ordered_marine_tag_list( + self) + + attack_nearest_max_allocation(self, max_allocation=self.max_allocation) + + marine_health_dict, are_there_friendly_marines, are_there_enemy_marines = observe_health(self, marine_tag_list=self.marine_tag_list) + + self.report_data = pd.DataFrame(marine_health_dict, index=[0]) + + if self.meta_meta_run_number is not None: + async with aiofiles.open(f"new_file{self.meta_meta_run_number}.csv", mode="w", encoding="utf-8", newline="") as afp: + writer = AsyncDictWriter(afp, self.report_data.columns, restval="NULL", quoting=csv.QUOTE_ALL) + await writer.writeheader() + await writer.writerows(self.report_data.to_dict(orient='records')) + + self.event_dict['started'].set() + + async def on_step(self, iteration): + are_there_friendly_marines = False + are_there_enemy_marines = False + if not self.event_dict['started'].is_set(): + print('waiting for on_start to happen') + await self.event_dict['started'].wait() + curr_time = time.perf_counter() + print('time diff - '+str(curr_time - self.on_step_time_list[-1])) + self.on_step_time_list.append( + curr_time + ) + + marine_health_dict = {} + + attack_nearest_max_allocation(self, max_allocation=self.max_allocation) + + marine_health_dict, are_there_friendly_marines, are_there_enemy_marines = observe_health( + self, marine_tag_list=self.marine_tag_list + ) + + self.report_data.loc[curr_time] = marine_health_dict + + self.report_data.fillna(0.0, inplace=True) + + if self.meta_meta_run_number is not None: + async with aiofiles.open(f"new_file{self.meta_meta_run_number}.csv", mode="w", encoding="utf-8", newline="") as afp: + writer = AsyncDictWriter(afp, self.report_data.columns, restval="NULL", quoting=csv.QUOTE_ALL) + await writer.writeheader() + await writer.writerows(self.report_data.to_dict(orient='records')) + + # if friendly or enemy marines health is 0, leave + if (not are_there_enemy_marines) or (not are_there_friendly_marines): + if self.history_dict is not None: + self.history_dict[self.run_index]['report_data'] = self.report_data.to_json() + + print(self.report_data) + print(self.report_data.iloc[-1, :].values) + + if self.result is not None: + self.result.append( + calc_value_func( + list( + self.report_data.iloc[-1, :].values + ) + ) + ) + print('results for this round is: '+str(self.result)) + + await self.client.leave() + + async def on_end(self, game_result: Result): + #self.report_data.to_csv('./marine_focus_fire_data/marine_health_data.csv') + print(f"{self.time_formatted} On end was called") + + +def main(): + run_game( + maps.get("Blistering Sands_marines2"), # maps.get("marines_4x4"), #maps.get("Blistering Sands_marines"), + [Bot(Race.Terran, MarineBot(max_allocation=6)), Computer(Race.Terran, Difficulty.VeryEasy)], + realtime=True, + #save_replay_as="ZvT.SC2Replay", + disable_fog=True + ) + + +if __name__ == "__main__": + #main() + import time + + start = time.time() + print("hello") + main() + end = time.time() + print(end - start) diff --git a/examples/terran/marine_meta_bot.py b/examples/terran/marine_faceoff/marine_meta_bot.py similarity index 100% rename from examples/terran/marine_meta_bot.py rename to examples/terran/marine_faceoff/marine_meta_bot.py diff --git a/examples/terran/marine_faceoff/model_orchestrator.py b/examples/terran/marine_faceoff/model_orchestrator.py new file mode 100644 index 00000000..d10aa5f8 --- /dev/null +++ b/examples/terran/marine_faceoff/model_orchestrator.py @@ -0,0 +1,25 @@ +import asyncio + + +class ModelOrchestrator: + """ + The orechstrator will implement the business logic behind training the model and + present the most updated version for inference. + """ + def __init__(self): + pass + + def fetch_latest_model(self): + pass + + def _init_training(self): + pass + + def train(self): + pass + + def update_observation(self): + pass + + def policy(self, observation): + return 1 \ No newline at end of file diff --git a/examples/terran/marine_faceoff/q_models.py b/examples/terran/marine_faceoff/q_models.py new file mode 100644 index 00000000..07d4981d --- /dev/null +++ b/examples/terran/marine_faceoff/q_models.py @@ -0,0 +1,275 @@ +import asyncio +import os +import sys +import json +from collections import OrderedDict + +import numpy as np +import tensorflow as tf +from tensorflow import keras +from tensorflow.keras import layers +from tensorflow import keras +from tensorflow.keras.utils import plot_model + +sys.path.append(os.path.join(os.path.dirname(__file__), "../..")) + + +from baselines.common.atari_wrappers import make_atari, wrap_deepmind +import numpy as np +import tensorflow as tf +from tensorflow import keras +from tensorflow.keras import layers + +# Configuration paramaters for the whole setup +seed = 42 +gamma = 0.99 # Discount factor for past rewards +epsilon = 1.0 # Epsilon greedy parameter +epsilon_min = 0.1 # Minimum epsilon greedy parameter +epsilon_max = 1.0 # Maximum epsilon greedy parameter +epsilon_interval = ( + epsilon_max - epsilon_min +) # Rate at which to reduce chance of random action being taken +batch_size = 32 # Size of batch taken from replay buffer +max_steps_per_episode = 10000 + +# Use the Baseline Atari environment because of Deepmind helper functions +env = make_atari("BreakoutNoFrameskip-v4") +# Warp the frames, grey scale, stake four frame and scale to smaller ratio +env = wrap_deepmind(env, frame_stack=True, scale=True) +env.seed(seed) + + +num_actions = 4 + + +def create_q_model_terran(): + # Network defined by the Deepmind paper + inputs = layers.Input(shape=(16, 1, 1,)) + num_friendly_marines = 8 + num_enemy_marines = 8 + num_actions = num_friendly_marines * num_enemy_marines + + # Convolutions on the frames on the screen + layer1 = layers.Flatten()(inputs) + layer2 = layers.Dense(64, activation="relu")(layer1) + layer3 = layers.Dense(num_actions, activation="relu")(layer2) + layer4 = layers.Reshape((num_friendly_marines, num_enemy_marines, 1))(layer3) + action = layers.Softmax(axis=1)(layer4) + + return keras.Model(inputs=inputs, outputs=action) + + +def create_q_model(): + # Network defined by the Deepmind paper + inputs = layers.Input(shape=(84, 84, 4,)) + + # Convolutions on the frames on the screen + layer1 = layers.Conv2D(32, 8, strides=4, activation="relu")(inputs) + layer2 = layers.Conv2D(64, 4, strides=2, activation="relu")(layer1) + layer3 = layers.Conv2D(64, 3, strides=1, activation="relu")(layer2) + + layer4 = layers.Flatten()(layer3) + + layer5 = layers.Dense(512, activation="relu")(layer4) + action = layers.Dense(num_actions, activation="linear")(layer5) + + return keras.Model(inputs=inputs, outputs=action) + + +# The first model makes the predictions for Q-values which are used to +# make a action. +model = create_q_model() +# Build a target model for the prediction of future rewards. +# The weights of a target model get updated every 10000 steps thus when the +# loss between the Q-values is calculated the target Q-value is stable. +model_target = create_q_model() + + +def create_q_sequential_model(): + # Network defined by the Deepmind paper + num_friendly_marines = 8 + num_enemy_marines = 8 + num_actions = num_friendly_marines * num_enemy_marines + + model = keras.Sequential( + [ + layers.Input(shape=(16, 1, 1,)), + layers.Flatten(), + layers.Dense(64, activation="relu"), + layers.Dense(num_actions, activation="relu"), + layers.Reshape((num_friendly_marines, num_enemy_marines, 1)), + layers.Softmax(axis=1) + ] + ) + + model.compile() + + return model + + +#def train_q_model(env, max_steps_per_episode, num_actions, model, epsilon_interval, epsilon_min, batch_size, model_target, gamma): + +# In the Deepmind paper they use RMSProp however then Adam optimizer +# improves training time +optimizer = keras.optimizers.Adam(learning_rate=0.00025, clipnorm=1.0) + +# Experience replay buffers +action_history = [] +state_history = [] +state_next_history = [] +rewards_history = [] +done_history = [] +episode_reward_history = [] +running_reward = 0 +episode_count = 0 +frame_count = 0 +# Number of frames to take random action and observe output +epsilon_random_frames = 50000 +# Number of frames for exploration +epsilon_greedy_frames = 1000000.0 +# Maximum replay length +# Note: The Deepmind paper suggests 1000000 however this causes memory issues +max_memory_length = 100000 +# Train the model after 4 actions +update_after_actions = 4 +# How often to update the target network +update_target_network = 10000 +# Using huber loss for stability +loss_function = keras.losses.Huber() + +while True: # Run until solved + state = np.array(env.reset()) + episode_reward = 0 + + for timestep in range(1, max_steps_per_episode): + # env.render(); Adding this line would show the attempts + # of the agent in a pop up window. + frame_count += 1 + + # Use epsilon-greedy for exploration + if frame_count < epsilon_random_frames or epsilon > np.random.rand(1)[0]: + # Take random action + action = np.random.choice(num_actions) + else: + # Predict action Q-values + # From environment state + state_tensor = tf.convert_to_tensor(state) + state_tensor = tf.expand_dims(state_tensor, 0) + action_probs = model(state_tensor, training=False) + # Take best action + action = tf.argmax(action_probs[0]).numpy() + + # Decay probability of taking random action + epsilon -= epsilon_interval / epsilon_greedy_frames + epsilon = max(epsilon, epsilon_min) + + # Apply the sampled action in our environment + state_next, reward, done, _ = env.step(action) + state_next = np.array(state_next) + + episode_reward += reward + + # Save actions and states in replay buffer + action_history.append(action) + state_history.append(state) + state_next_history.append(state_next) + done_history.append(done) + rewards_history.append(reward) + state = state_next + + # Update every fourth frame and once batch size is over 32 + if frame_count % update_after_actions == 0 and len(done_history) > batch_size: + # Get indices of samples for replay buffers + indices = np.random.choice(range(len(done_history)), size=batch_size) + + # Using list comprehension to sample from replay buffer + state_sample = np.array([state_history[i] for i in indices]) + state_next_sample = np.array([state_next_history[i] for i in indices]) + rewards_sample = [rewards_history[i] for i in indices] + action_sample = [action_history[i] for i in indices] + done_sample = tf.convert_to_tensor( + [float(done_history[i]) for i in indices] + ) + + # Build the updated Q-values for the sampled future states + # Use the target model for stability + future_rewards = model_target.predict(state_next_sample) + # Q value = reward + discount factor * expected future reward + updated_q_values = rewards_sample + gamma * tf.reduce_max( + future_rewards, axis=1 + ) + + # If final frame set the last value to -1 + updated_q_values = updated_q_values * (1 - done_sample) - done_sample + + # Create a mask so we only calculate loss on the updated Q-values + masks = tf.one_hot(action_sample, num_actions) + + with tf.GradientTape() as tape: + # Train the model on the states and updated Q-values + q_values = model(state_sample) + + # Apply the masks to the Q-values to get the Q-value for action taken + q_action = tf.reduce_sum(tf.multiply(q_values, masks), axis=1) + # Calculate loss between new Q-value and old Q-value + loss = loss_function(updated_q_values, q_action) + + # Backpropagation + grads = tape.gradient(loss, model.trainable_variables) + optimizer.apply_gradients(zip(grads, model.trainable_variables)) + + if frame_count % update_target_network == 0: + # update the the target network with new weights + model_target.set_weights(model.get_weights()) + # Log details + template = "running reward: {:.2f} at episode {}, frame count {}" + print(template.format(running_reward, episode_count, frame_count)) + + # Limit the state and reward history + if len(rewards_history) > max_memory_length: + del rewards_history[:1] + del state_history[:1] + del state_next_history[:1] + del action_history[:1] + del done_history[:1] + + if done: + break + + # Update running reward to check condition for solving + episode_reward_history.append(episode_reward) + if len(episode_reward_history) > 100: + del episode_reward_history[:1] + running_reward = np.mean(episode_reward_history) + + episode_count += 1 + print(f"episode count is {episode_count}") + + if running_reward > 40: # Condition to consider the task solved + print("Solved at episode {}!".format(episode_count)) + break + + + +def main(): + X_train = np.random.randint( + low=0, high=45, size=(1000, 16, 1, 1) + ) + y_train = np.random.rand(1000, 16, 1, 1) + + X_test = np.random.randint( + low=0, high=45, size=(1000, 16, 1, 1) + ) + y_test = np.random.rand(1000, 16, 1, 1) + + model_sequential: keras.Sequential = create_q_sequential_model() + + print(model_sequential.predict(X_train)) + + # model_sequential.train( + # + # ) + + +if __name__ == '__main__': + main() \ No newline at end of file From 89c42cd9ca6b7ed1ee0c7daf209398ce722ad4e3 Mon Sep 17 00:00:00 2001 From: ShaharKatz Date: Tue, 7 Feb 2023 12:18:10 +0200 Subject: [PATCH 10/11] personal code to the marine faceoff bot --- .../terran/marine_faceoff/marine_gym_bot.py | 2 +- .../marine_faceoff/marine_helper_funcs.py | 2 +- .../terran/marine_faceoff/marine_match_bot.py | 4 +- .../terran/marine_faceoff/marine_meta_bot.py | 6 +- examples/terran/marine_faceoff/q_models.py | 265 ++++++++++-------- 5 files changed, 162 insertions(+), 117 deletions(-) diff --git a/examples/terran/marine_faceoff/marine_gym_bot.py b/examples/terran/marine_faceoff/marine_gym_bot.py index 9df6fbcb..75d0a464 100644 --- a/examples/terran/marine_faceoff/marine_gym_bot.py +++ b/examples/terran/marine_faceoff/marine_gym_bot.py @@ -1,7 +1,7 @@ import os import sys -sys.path.append(os.path.join(os.path.dirname(__file__), "../..")) +sys.path.append(os.path.join(os.path.dirname(__file__), "../../..")) # https://www.youtube.com/watch?v=1-QtVxce44k&ab_channel=Altercate diff --git a/examples/terran/marine_faceoff/marine_helper_funcs.py b/examples/terran/marine_faceoff/marine_helper_funcs.py index a7d06919..cf6d61d3 100644 --- a/examples/terran/marine_faceoff/marine_helper_funcs.py +++ b/examples/terran/marine_faceoff/marine_helper_funcs.py @@ -19,7 +19,7 @@ from tensorflow.keras import layers from tensorflow.keras.utils import plot_model -sys.path.append(os.path.join(os.path.dirname(__file__), "../..")) +sys.path.append(os.path.join(os.path.dirname(__file__), "../../..")) import numpy as np diff --git a/examples/terran/marine_faceoff/marine_match_bot.py b/examples/terran/marine_faceoff/marine_match_bot.py index 9df6fbcb..b52fe106 100644 --- a/examples/terran/marine_faceoff/marine_match_bot.py +++ b/examples/terran/marine_faceoff/marine_match_bot.py @@ -1,11 +1,11 @@ import os import sys -sys.path.append(os.path.join(os.path.dirname(__file__), "../..")) +sys.path.append(os.path.join(os.path.dirname(__file__), "../../..")) # https://www.youtube.com/watch?v=1-QtVxce44k&ab_channel=Altercate -from marine_helper_funcs import * +from examples.terran.marine_faceoff.marine_helper_funcs import * class MarineBot(BotAI): diff --git a/examples/terran/marine_faceoff/marine_meta_bot.py b/examples/terran/marine_faceoff/marine_meta_bot.py index 0bee9d89..bb9c9f52 100644 --- a/examples/terran/marine_faceoff/marine_meta_bot.py +++ b/examples/terran/marine_faceoff/marine_meta_bot.py @@ -1,8 +1,8 @@ -from marine_helper_funcs import * +from examples.terran.marine_faceoff.marine_helper_funcs import * -from examples.terran.marine_match_bot import MarineBot +from examples.terran.marine_faceoff.marine_match_bot import MarineBot -sys.path.append(os.path.join(os.path.dirname(__file__), "../..")) +sys.path.append(os.path.join(os.path.dirname(__file__), "../../..")) class MarineMetaBot: diff --git a/examples/terran/marine_faceoff/q_models.py b/examples/terran/marine_faceoff/q_models.py index 07d4981d..167e1285 100644 --- a/examples/terran/marine_faceoff/q_models.py +++ b/examples/terran/marine_faceoff/q_models.py @@ -11,7 +11,9 @@ from tensorflow import keras from tensorflow.keras.utils import plot_model -sys.path.append(os.path.join(os.path.dirname(__file__), "../..")) +sys.path.append(os.path.join(os.path.dirname(__file__), "../../..")) + + from baselines.common.atari_wrappers import make_atari, wrap_deepmind @@ -20,6 +22,8 @@ from tensorflow import keras from tensorflow.keras import layers +from marine_helper_funcs import calc_value_func + # Configuration paramaters for the whole setup seed = 42 gamma = 0.99 # Discount factor for past rewards @@ -137,121 +141,133 @@ def create_q_sequential_model(): # Using huber loss for stability loss_function = keras.losses.Huber() -while True: # Run until solved - state = np.array(env.reset()) - episode_reward = 0 - - for timestep in range(1, max_steps_per_episode): - # env.render(); Adding this line would show the attempts - # of the agent in a pop up window. - frame_count += 1 - - # Use epsilon-greedy for exploration - if frame_count < epsilon_random_frames or epsilon > np.random.rand(1)[0]: - # Take random action - action = np.random.choice(num_actions) - else: - # Predict action Q-values - # From environment state - state_tensor = tf.convert_to_tensor(state) - state_tensor = tf.expand_dims(state_tensor, 0) - action_probs = model(state_tensor, training=False) - # Take best action - action = tf.argmax(action_probs[0]).numpy() - - # Decay probability of taking random action - epsilon -= epsilon_interval / epsilon_greedy_frames - epsilon = max(epsilon, epsilon_min) - - # Apply the sampled action in our environment - state_next, reward, done, _ = env.step(action) - state_next = np.array(state_next) - - episode_reward += reward - - # Save actions and states in replay buffer - action_history.append(action) - state_history.append(state) - state_next_history.append(state_next) - done_history.append(done) - rewards_history.append(reward) - state = state_next - - # Update every fourth frame and once batch size is over 32 - if frame_count % update_after_actions == 0 and len(done_history) > batch_size: - # Get indices of samples for replay buffers - indices = np.random.choice(range(len(done_history)), size=batch_size) - - # Using list comprehension to sample from replay buffer - state_sample = np.array([state_history[i] for i in indices]) - state_next_sample = np.array([state_next_history[i] for i in indices]) - rewards_sample = [rewards_history[i] for i in indices] - action_sample = [action_history[i] for i in indices] - done_sample = tf.convert_to_tensor( - [float(done_history[i]) for i in indices] - ) - - # Build the updated Q-values for the sampled future states - # Use the target model for stability - future_rewards = model_target.predict(state_next_sample) - # Q value = reward + discount factor * expected future reward - updated_q_values = rewards_sample + gamma * tf.reduce_max( - future_rewards, axis=1 - ) - - # If final frame set the last value to -1 - updated_q_values = updated_q_values * (1 - done_sample) - done_sample - - # Create a mask so we only calculate loss on the updated Q-values - masks = tf.one_hot(action_sample, num_actions) - - with tf.GradientTape() as tape: - # Train the model on the states and updated Q-values - q_values = model(state_sample) - - # Apply the masks to the Q-values to get the Q-value for action taken - q_action = tf.reduce_sum(tf.multiply(q_values, masks), axis=1) - # Calculate loss between new Q-value and old Q-value - loss = loss_function(updated_q_values, q_action) - - # Backpropagation - grads = tape.gradient(loss, model.trainable_variables) - optimizer.apply_gradients(zip(grads, model.trainable_variables)) - - if frame_count % update_target_network == 0: - # update the the target network with new weights - model_target.set_weights(model.get_weights()) - # Log details - template = "running reward: {:.2f} at episode {}, frame count {}" - print(template.format(running_reward, episode_count, frame_count)) - - # Limit the state and reward history - if len(rewards_history) > max_memory_length: - del rewards_history[:1] - del state_history[:1] - del state_next_history[:1] - del action_history[:1] - del done_history[:1] - - if done: +def run_q_training(frame_count=None, episode_count=None): + while True: # Run until solved + state = np.array(env.reset()) + episode_reward = 0 + + for timestep in range(1, max_steps_per_episode): + # env.render(); Adding this line would show the attempts + # of the agent in a pop up window. + frame_count += 1 + + # Use epsilon-greedy for exploration + if frame_count < epsilon_random_frames or epsilon > np.random.rand(1)[0]: + # Take random action + action = np.random.choice(num_actions) + else: + # Predict action Q-values + # From environment state + state_tensor = tf.convert_to_tensor(state) + state_tensor = tf.expand_dims(state_tensor, 0) + action_probs = model(state_tensor, training=False) + # Take best action + action = tf.argmax(action_probs[0]).numpy() + + # Decay probability of taking random action + epsilon -= epsilon_interval / epsilon_greedy_frames + epsilon = max(epsilon, epsilon_min) + + # Apply the sampled action in our environment + state_next, reward, done, _ = env.step(action) + state_next = np.array(state_next) + + episode_reward += reward + + # Save actions and states in replay buffer + action_history.append(action) + state_history.append(state) + state_next_history.append(state_next) + done_history.append(done) + rewards_history.append(reward) + state = state_next + + # Update every fourth frame and once batch size is over 32 + if frame_count % update_after_actions == 0 and len(done_history) > batch_size: + # Get indices of samples for replay buffers + indices = np.random.choice(range(len(done_history)), size=batch_size) + + # Using list comprehension to sample from replay buffer + state_sample = np.array([state_history[i] for i in indices]) + state_next_sample = np.array([state_next_history[i] for i in indices]) + rewards_sample = [rewards_history[i] for i in indices] + action_sample = [action_history[i] for i in indices] + done_sample = tf.convert_to_tensor( + [float(done_history[i]) for i in indices] + ) + + # Build the updated Q-values for the sampled future states + # Use the target model for stability + future_rewards = model_target.predict(state_next_sample) + # Q value = reward + discount factor * expected future reward + updated_q_values = rewards_sample + gamma * tf.reduce_max( + future_rewards, axis=1 + ) + + # If final frame set the last value to -1 + updated_q_values = updated_q_values * (1 - done_sample) - done_sample + + # Create a mask so we only calculate loss on the updated Q-values + masks = tf.one_hot(action_sample, num_actions) + + with tf.GradientTape() as tape: + # Train the model on the states and updated Q-values + q_values = model(state_sample) + + # Apply the masks to the Q-values to get the Q-value for action taken + q_action = tf.reduce_sum(tf.multiply(q_values, masks), axis=1) + # Calculate loss between new Q-value and old Q-value + loss = loss_function(updated_q_values, q_action) + + # Backpropagation + grads = tape.gradient(loss, model.trainable_variables) + optimizer.apply_gradients(zip(grads, model.trainable_variables)) + + if frame_count % update_target_network == 0: + # update the the target network with new weights + model_target.set_weights(model.get_weights()) + # Log details + template = "running reward: {:.2f} at episode {}, frame count {}" + print(template.format(running_reward, episode_count, frame_count)) + + # Limit the state and reward history + if len(rewards_history) > max_memory_length: + del rewards_history[:1] + del state_history[:1] + del state_next_history[:1] + del action_history[:1] + del done_history[:1] + + if done: + break + + # Update running reward to check condition for solving + episode_reward_history.append(episode_reward) + if len(episode_reward_history) > 100: + del episode_reward_history[:1] + running_reward = np.mean(episode_reward_history) + + episode_count += 1 + print(f"episode count is {episode_count}") + + if running_reward > 40: # Condition to consider the task solved + print("Solved at episode {}!".format(episode_count)) break - # Update running reward to check condition for solving - episode_reward_history.append(episode_reward) - if len(episode_reward_history) > 100: - del episode_reward_history[:1] - running_reward = np.mean(episode_reward_history) - episode_count += 1 - print(f"episode count is {episode_count}") +def mock_random_state(N=1000): + return np.random.randint( + low=0, high=45, size=(N, 16, 1, 1) + ) - if running_reward > 40: # Condition to consider the task solved - print("Solved at episode {}!".format(episode_count)) - break +def mock_random_reward(): + return calc_value_func( + mock_random_state() + ) -def main(): +def main_model_predict(): X_train = np.random.randint( low=0, high=45, size=(1000, 16, 1, 1) ) @@ -271,5 +287,34 @@ def main(): # ) +def main_model_train(): + model_sequential: keras.Sequential = create_q_sequential_model() + state: tf.Tensor = tf.convert_to_tensor( + mock_random_state(1000) + ) + + state_history = [ + tf.convert_to_tensor( + mock_random_state(N=1) + ) for i in range(0, 1000) + ] + + with tf.GradientTape() as tape: + # Train the model on the states and updated Q-values + q_values = model_sequential( + np.squeeze( + state_history, + axis=1 + ) + ) + + q_action = tf.reduce_sum(q_values, axis=2) # TODO: is it axis=1 or axis=2 + + # Calculate loss between new Q-value and old Q-value + loss = loss_function(updated_q_values, q_action) + + print(tape.watched_variables()) + + if __name__ == '__main__': - main() \ No newline at end of file + main_model_predict() From 2d01b9fff0b12cfa7f0ef2b5c7887d7e7df8b114 Mon Sep 17 00:00:00 2001 From: ShaharKatz Date: Tue, 7 Feb 2023 16:49:42 +0200 Subject: [PATCH 11/11] adding atari and breakout demo --- .../terran/marine_faceoff/atari_breakout.py | 208 ++++++++ .../terran/marine_faceoff/breakout_demo.py | 210 ++++++++ .../marine_faceoff/data/arms_results2.csv | 101 ++++ .../marine_faceoff/data/history_dict2.json | 502 ++++++++++++++++++ 4 files changed, 1021 insertions(+) create mode 100644 examples/terran/marine_faceoff/atari_breakout.py create mode 100644 examples/terran/marine_faceoff/breakout_demo.py create mode 100644 examples/terran/marine_faceoff/data/arms_results2.csv create mode 100644 examples/terran/marine_faceoff/data/history_dict2.json diff --git a/examples/terran/marine_faceoff/atari_breakout.py b/examples/terran/marine_faceoff/atari_breakout.py new file mode 100644 index 00000000..541264aa --- /dev/null +++ b/examples/terran/marine_faceoff/atari_breakout.py @@ -0,0 +1,208 @@ +import os +import sys + +sys.path.append(os.path.join(os.path.dirname(__file__), "../../..")) + +# -------------------------------------------------------------------------------------- +## Setup ## +# ========# +from baselines.common.atari_wrappers import make_atari, wrap_deepmind +import numpy as np +import tensorflow as tf +from tensorflow import keras +from tensorflow.keras import layers + +# Configuration paramaters for the whole setup +seed = 42 +gamma = 0.99 # Discount factor for past rewards +epsilon = 1.0 # Epsilon greedy parameter +epsilon_min = 0.1 # Minimum epsilon greedy parameter +epsilon_max = 1.0 # Maximum epsilon greedy parameter +epsilon_interval = ( + epsilon_max - epsilon_min +) # Rate at which to reduce chance of random action being taken +batch_size = 32 # Size of batch taken from replay buffer +max_steps_per_episode = 10000 + +# Use the Baseline Atari environment because of Deepmind helper functions +env = make_atari("BreakoutNoFrameskip-v4") +# Warp the frames, grey scale, stake four frame and scale to smaller ratio +env = wrap_deepmind(env, frame_stack=True, scale=True) +env.seed(seed) +# -------------------------------------------------------------------------------------- + +## Implement the Deep Q-Network ## +# ===============================# + +num_actions = 4 + + +def create_q_model(): + # Network defined by the Deepmind paper + inputs = layers.Input(shape=(84, 84, 4,)) + + # Convolutions on the frames on the screen + layer1 = layers.Conv2D(32, 8, strides=4, activation="relu")(inputs) + layer2 = layers.Conv2D(64, 4, strides=2, activation="relu")(layer1) + layer3 = layers.Conv2D(64, 3, strides=1, activation="relu")(layer2) + + layer4 = layers.Flatten()(layer3) + + layer5 = layers.Dense(512, activation="relu")(layer4) + action = layers.Dense(num_actions, activation="linear")(layer5) + + return keras.Model(inputs=inputs, outputs=action) + + +# The first model makes the predictions for Q-values which are used to +# make a action. +model = create_q_model() +# Build a target model for the prediction of future rewards. +# The weights of a target model get updated every 10000 steps thus when the +# loss between the Q-values is calculated the target Q-value is stable. +model_target = create_q_model() +# -------------------------------------------------------------------------------------- + + +## Train ## +# ========# +# In the Deepmind paper they use RMSProp however then Adam optimizer +# improves training time +optimizer = keras.optimizers.Adam(learning_rate=0.00025, clipnorm=1.0) + +# Experience replay buffers +action_history = [] +state_history = [] +state_next_history = [] +rewards_history = [] +done_history = [] +episode_reward_history = [] +running_reward = 0 +episode_count = 0 +frame_count = 0 +# Number of frames to take random action and observe output +epsilon_random_frames = 50000 +# Number of frames for exploration +epsilon_greedy_frames = 1000000.0 +# Maximum replay length +# Note: The Deepmind paper suggests 1000000 however this causes memory issues +max_memory_length = 100000 +# Train the model after 4 actions +update_after_actions = 4 +# How often to update the target network +update_target_network = 10000 +# Using huber loss for stability +loss_function = keras.losses.Huber() + +while True: # Run until solved + state = np.array(env.reset()) + episode_reward = 0 + + for timestep in range(1, max_steps_per_episode): + # env.render(); Adding this line would show the attempts + # of the agent in a pop up window. + frame_count += 1 + + # Use epsilon-greedy for exploration + if frame_count < epsilon_random_frames or epsilon > np.random.rand(1)[0]: + # Take random action + action = np.random.choice(num_actions) + else: + # Predict action Q-values + # From environment state + state_tensor = tf.convert_to_tensor(state) + state_tensor = tf.expand_dims(state_tensor, 0) + action_probs = model(state_tensor, training=False) + # Take best action + action = tf.argmax(action_probs[0]).numpy() + + # Decay probability of taking random action + epsilon -= epsilon_interval / epsilon_greedy_frames + epsilon = max(epsilon, epsilon_min) + + # Apply the sampled action in our environment + state_next, reward, done, _ = env.step(action) + state_next = np.array(state_next) + + episode_reward += reward + + # Save actions and states in replay buffer + action_history.append(action) + state_history.append(state) + state_next_history.append(state_next) + done_history.append(done) + rewards_history.append(reward) + state = state_next + + # Update every fourth frame and once batch size is over 32 + if frame_count % update_after_actions == 0 and len(done_history) > batch_size: + # Get indices of samples for replay buffers + indices = np.random.choice(range(len(done_history)), size=batch_size) + + # Using list comprehension to sample from replay buffer + state_sample = np.array([state_history[i] for i in indices]) + state_next_sample = np.array([state_next_history[i] for i in indices]) + rewards_sample = [rewards_history[i] for i in indices] + action_sample = [action_history[i] for i in indices] + done_sample = tf.convert_to_tensor( + [float(done_history[i]) for i in indices] + ) + + # Build the updated Q-values for the sampled future states + # Use the target model for stability + future_rewards = model_target.predict(state_next_sample) + # Q value = reward + discount factor * expected future reward + updated_q_values = rewards_sample + gamma * tf.reduce_max( + future_rewards, axis=1 + ) + + # If final frame set the last value to -1 + updated_q_values = updated_q_values * (1 - done_sample) - done_sample + + # Create a mask so we only calculate loss on the updated Q-values + masks = tf.one_hot(action_sample, num_actions) + + with tf.GradientTape() as tape: + # Train the model on the states and updated Q-values + q_values = model(state_sample) + + # Apply the masks to the Q-values to get the Q-value for action taken + q_action = tf.reduce_sum(tf.multiply(q_values, masks), axis=1) + # Calculate loss between new Q-value and old Q-value + loss = loss_function(updated_q_values, q_action) + + # Backpropagation + grads = tape.gradient(loss, model.trainable_variables) + optimizer.apply_gradients(zip(grads, model.trainable_variables)) + + if frame_count % update_target_network == 0: + # update the the target network with new weights + model_target.set_weights(model.get_weights()) + # Log details + template = "running reward: {:.2f} at episode {}, frame count {}" + print(template.format(running_reward, episode_count, frame_count)) + + # Limit the state and reward history + if len(rewards_history) > max_memory_length: + del rewards_history[:1] + del state_history[:1] + del state_next_history[:1] + del action_history[:1] + del done_history[:1] + + if done: + break + + # Update running reward to check condition for solving + episode_reward_history.append(episode_reward) + if len(episode_reward_history) > 100: + del episode_reward_history[:1] + running_reward = np.mean(episode_reward_history) + + episode_count += 1 + print(f"episode count is {episode_count}") + + if running_reward > 40: # Condition to consider the task solved + print("Solved at episode {}!".format(episode_count)) + break +# -------------------------------------------------------------------------------------- diff --git a/examples/terran/marine_faceoff/breakout_demo.py b/examples/terran/marine_faceoff/breakout_demo.py new file mode 100644 index 00000000..624f787e --- /dev/null +++ b/examples/terran/marine_faceoff/breakout_demo.py @@ -0,0 +1,210 @@ +import asyncio +import os +import sys +import json +from collections import OrderedDict + +import numpy as np +import tensorflow as tf +from tensorflow import keras +from tensorflow.keras import layers +from tensorflow import keras +from tensorflow.keras.utils import plot_model + +sys.path.append(os.path.join(os.path.dirname(__file__), "../../..")) + + +from baselines.common.atari_wrappers import make_atari, wrap_deepmind +import numpy as np +import tensorflow as tf +from tensorflow import keras +from tensorflow.keras import layers + +# Configuration paramaters for the whole setup +seed = 42 +gamma = 0.99 # Discount factor for past rewards +epsilon = 1.0 # Epsilon greedy parameter +epsilon_min = 0.1 # Minimum epsilon greedy parameter +epsilon_max = 1.0 # Maximum epsilon greedy parameter +epsilon_interval = ( + epsilon_max - epsilon_min +) # Rate at which to reduce chance of random action being taken +batch_size = 32 # Size of batch taken from replay buffer +max_steps_per_episode = 10000 + +# Use the Baseline Atari environment because of Deepmind helper functions +env = make_atari("BreakoutNoFrameskip-v4") +# Warp the frames, grey scale, stake four frame and scale to smaller ratio +env = wrap_deepmind(env, frame_stack=True, scale=True) +env.seed(seed) + + +num_actions = 4 + + +def create_q_model(): + # Network defined by the Deepmind paper + inputs = layers.Input(shape=(84, 84, 4,)) + + # Convolutions on the frames on the screen + layer1 = layers.Conv2D(32, 8, strides=4, activation="relu")(inputs) + layer2 = layers.Conv2D(64, 4, strides=2, activation="relu")(layer1) + layer3 = layers.Conv2D(64, 3, strides=1, activation="relu")(layer2) + + layer4 = layers.Flatten()(layer3) + + layer5 = layers.Dense(512, activation="relu")(layer4) + action = layers.Dense(num_actions, activation="linear")(layer5) + + return keras.Model(inputs=inputs, outputs=action) + + +# The first model makes the predictions for Q-values which are used to +# make a action. +model = create_q_model() +# Build a target model for the prediction of future rewards. +# The weights of a target model get updated every 10000 steps thus when the +# loss between the Q-values is calculated the target Q-value is stable. +model_target = create_q_model() + + + +# In the Deepmind paper they use RMSProp however then Adam optimizer +# improves training time +optimizer = keras.optimizers.Adam(learning_rate=0.00025, clipnorm=1.0) + +# Experience replay buffers +action_history = [] +state_history = [] +state_next_history = [] +rewards_history = [] +done_history = [] +episode_reward_history = [] +running_reward = 0 +episode_count = 0 +frame_count = 0 +# Number of frames to take random action and observe output +epsilon_random_frames = 50000 +# Number of frames for exploration +epsilon_greedy_frames = 1000000.0 +# Maximum replay length +# Note: The Deepmind paper suggests 1000000 however this causes memory issues +max_memory_length = 100000 +# Train the model after 4 actions +update_after_actions = 4 +# How often to update the target network +update_target_network = 10000 +# Using huber loss for stability +loss_function = keras.losses.Huber() + +while True: # Run until solved + state = np.array(env.reset()) + episode_reward = 0 + + for timestep in range(1, max_steps_per_episode): + # env.render(); Adding this line would show the attempts + # of the agent in a pop up window. + frame_count += 1 + + # Use epsilon-greedy for exploration + if frame_count < epsilon_random_frames or epsilon > np.random.rand(1)[0]: + # Take random action + action = np.random.choice(num_actions) + else: + # Predict action Q-values + # From environment state + state_tensor = tf.convert_to_tensor(state) + state_tensor = tf.expand_dims(state_tensor, 0) + action_probs = model(state_tensor, training=False) + # Take best action + action = tf.argmax(action_probs[0]).numpy() + + # Decay probability of taking random action + epsilon -= epsilon_interval / epsilon_greedy_frames + epsilon = max(epsilon, epsilon_min) + + # Apply the sampled action in our environment + state_next, reward, done, _ = env.step(action) + state_next = np.array(state_next) + + episode_reward += reward + + # Save actions and states in replay buffer + action_history.append(action) + state_history.append(state) + state_next_history.append(state_next) + done_history.append(done) + rewards_history.append(reward) + state = state_next + + # Update every fourth frame and once batch size is over 32 + if frame_count % update_after_actions == 0 and len(done_history) > batch_size: + # Get indices of samples for replay buffers + indices = np.random.choice(range(len(done_history)), size=batch_size) + + # Using list comprehension to sample from replay buffer + state_sample = np.array([state_history[i] for i in indices]) + state_next_sample = np.array([state_next_history[i] for i in indices]) + rewards_sample = [rewards_history[i] for i in indices] + action_sample = [action_history[i] for i in indices] + done_sample = tf.convert_to_tensor( + [float(done_history[i]) for i in indices] + ) + + # Build the updated Q-values for the sampled future states + # Use the target model for stability + future_rewards = model_target.predict(state_next_sample) + # Q value = reward + discount factor * expected future reward + updated_q_values = rewards_sample + gamma * tf.reduce_max( + future_rewards, axis=1 + ) + + # If final frame set the last value to -1 + updated_q_values = updated_q_values * (1 - done_sample) - done_sample + + # Create a mask so we only calculate loss on the updated Q-values + masks = tf.one_hot(action_sample, num_actions) + + with tf.GradientTape() as tape: + # Train the model on the states and updated Q-values + q_values = model(state_sample) + + # Apply the masks to the Q-values to get the Q-value for action taken + q_action = tf.reduce_sum(tf.multiply(q_values, masks), axis=1) + # Calculate loss between new Q-value and old Q-value + loss = loss_function(updated_q_values, q_action) + + # Backpropagation + grads = tape.gradient(loss, model.trainable_variables) + optimizer.apply_gradients(zip(grads, model.trainable_variables)) + + if frame_count % update_target_network == 0: + # update the the target network with new weights + model_target.set_weights(model.get_weights()) + # Log details + template = "running reward: {:.2f} at episode {}, frame count {}" + print(template.format(running_reward, episode_count, frame_count)) + + # Limit the state and reward history + if len(rewards_history) > max_memory_length: + del rewards_history[:1] + del state_history[:1] + del state_next_history[:1] + del action_history[:1] + del done_history[:1] + + if done: + break + + # Update running reward to check condition for solving + episode_reward_history.append(episode_reward) + if len(episode_reward_history) > 100: + del episode_reward_history[:1] + running_reward = np.mean(episode_reward_history) + + episode_count += 1 + print(f"episode count is {episode_count}") + + if running_reward > 40: # Condition to consider the task solved + print("Solved at episode {}!".format(episode_count)) + break \ No newline at end of file diff --git a/examples/terran/marine_faceoff/data/arms_results2.csv b/examples/terran/marine_faceoff/data/arms_results2.csv new file mode 100644 index 00000000..d0cf4762 --- /dev/null +++ b/examples/terran/marine_faceoff/data/arms_results2.csv @@ -0,0 +1,101 @@ +,max_allocation,value +0,1.0,-111.0 +1,1.0,-123.0 +2,1.0,-114.0 +3,1.0,-117.0 +4,1.0,-75.0 +5,1.0,-114.0 +6,1.0,-99.0 +7,1.0,-129.0 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