agents.py

"""Implement Agents and Environments (Chapters 1-2).

The class hierarchies are as follows:

Thing ## A physical object that can exist in an environment
    Agent
        Wumpus
    Dirt
    Wall
    ...

Environment ## An environment holds objects, runs simulations
    XYEnvironment
        VacuumEnvironment
        WumpusEnvironment

An agent program is a callable instance, taking percepts and choosing actions
    SimpleReflexAgentProgram
    ...

EnvGUI ## A window with a graphical representation of the Environment

EnvToolbar ## contains buttons for controlling EnvGUI

EnvCanvas ## Canvas to display the environment of an EnvGUI

"""

# TO DO:
# Implement grabbing correctly.
# When an object is grabbed, does it still have a location?
# What if it is released?
# What if the grabbed or the grabber is deleted?
# What if the grabber moves?
#
# Speed control in GUI does not have any effect -- fix it.

from grid import distance2
from statistics import mean

import random
import copy
import collections

# ______________________________________________________________________________


class Thing(object):

    """This represents any physical object that can appear in an Environment.
    You subclass Thing to get the things you want.  Each thing can have a
    .__name__  slot (used for output only)."""

    def __repr__(self):
        return '<{}>'.format(getattr(self, '__name__', self.__class__.__name__))

    def is_alive(self):
        "Things that are 'alive' should return true."
        return hasattr(self, 'alive') and self.alive

    def show_state(self):
        "Display the agent's internal state.  Subclasses should override."
        print("I don't know how to show_state.")

    # def display(self, canvas, x, y, width, height):
    #     # Do we need this?
    #     "Display an image of this Thing on the canvas."
    #     pass

    def isVisible(self):
        "Override to make a Thing invisible."
        return True

    # def getImage(self):
    #     '''Creates an image based on the object name.
    #     Override to use custom objects.'''
    #     name = '???'
    #     img = Image.new('RGB', (200, 100))
    #     d = ImageDraw.Draw(img)
    #     d.text((20, 20), name, fill=(255, 0, 0))
    #     return d


class Agent(Thing):

    """An Agent is a subclass of Thing with one required slot,
    .program, which should hold a function that takes one argument, the
    percept, and returns an action. (What counts as a percept or action
    will depend on the specific environment in which the agent exists.)
    Note that 'program' is a slot, not a method.  If it were a method,
    then the program could 'cheat' and look at aspects of the agent.
    It's not supposed to do that: the program can only look at the
    percepts.  An agent program that needs a model of the world (and of
    the agent itself) will have to build and maintain its own model.
    There is an optional slot, .performance, which is a number giving
    the performance measure of the agent in its environment."""

    def __init__(self, program=None):
        self.alive = True
        self.bump = False
        self.holding = []
        self.performance = 0
        if program is None:
            def program(percept):
                return eval(input('Percept={}; action? ' .format(percept)))
        assert isinstance(program, collections.Callable)
        self.program = program

    def can_grab(self, thing):
        """Returns True if this agent can grab this thing.
        Override for appropriate subclasses of Agent and Thing."""
        return False


def TraceAgent(agent):
    """Wrap the agent's program to print its input and output. This will let
    you see what the agent is doing in the environment."""
    old_program = agent.program

    def new_program(percept):
        action = old_program(percept)
        print('{} perceives {} and does {}'.format(agent, percept, action))
        return action
    agent.program = new_program
    return agent

# ______________________________________________________________________________


def TableDrivenAgentProgram(table):
    """This agent selects an action based on the percept sequence.
    It is practical only for tiny domains.
    To customize it, provide as table a dictionary of all
    {percept_sequence:action} pairs. [Figure 2.7]"""
    percepts = []

    def program(percept):
        percepts.append(percept)
        action = table.get(tuple(percepts))
        return action
    return program


def RandomAgentProgram(actions):
    "An agent that chooses an action at random, ignoring all percepts."
    return lambda percept: random.choice(actions)

# ______________________________________________________________________________


def SimpleReflexAgentProgram(rules, interpret_input):
    "This agent takes action based solely on the percept. [Figure 2.10]"
    def program(percept):
        state = interpret_input(percept)
        rule = rule_match(state, rules)
        action = rule.action
        return action
    return program


def ModelBasedReflexAgentProgram(rules, update_state):
    "This agent takes action based on the percept and state. [Figure 2.12]"
    def program(percept):
        program.state = update_state(program.state, program.action, percept)
        rule = rule_match(program.state, rules)
        action = rule.action
        return action
    program.state = program.action = None
    return program


def rule_match(state, rules):
    "Find the first rule that matches state."
    for rule in rules:
        if rule.matches(state):
            return rule

# ______________________________________________________________________________


class Environment(object):

    """Abstract class representing an Environment.  'Real' Environment classes
    inherit from this. Your Environment will typically need to implement:
        percept:           Define the percept that an agent sees.
        execute_action:    Define the effects of executing an action.
                           Also update the agent.performance slot.
    The environment keeps a list of .things and .agents (which is a subset
    of .things). Each agent has a .performance slot, initialized to 0.
    Each thing has a .location slot, even though some environments may not
    need this."""

    def __init__(self):
        self.things = []
        self.agents = []

    def thing_classes(self):
        return []  # List of classes that can go into environment

    def percept(self, agent):
        '''
            Return the percept that the agent sees at this point.
            (Implement this.)
        '''
        raise NotImplementedError

    def execute_action(self, agent, action):
        "Change the world to reflect this action. (Implement this.)"
        raise NotImplementedError

    def default_location(self, thing):
        "Default location to place a new thing with unspecified location."
        return None

    def exogenous_change(self):
        "If there is spontaneous change in the world, override this."
        pass

    def is_done(self):
        "By default, we're done when we can't find a live agent."
        return not any(agent.is_alive() for agent in self.agents)

    def step(self):
        """Run the environment for one time step. If the
        actions and exogenous changes are independent, this method will
        do.  If there are interactions between them, you'll need to
        override this method."""
        if not self.is_done():
            actions = []
            for agent in self.agents:
                if agent.alive:
                    actions.append(agent.program(self.percept(agent)))
                else:
                    actions.append("")
            for (agent, action) in zip(self.agents, actions):
                self.execute_action(agent, action)
            self.exogenous_change()

    def run(self, steps=1000):
        "Run the Environment for given number of time steps."
        for step in range(steps):
            if self.is_done():
                return
            self.step()

    def is_thing_at(self, location, tclass=Thing):
        "Return all things exactly at a given location."
        for thing in self.things:
            if thing.location == location and isinstance(thing, tclass):
                return True
        return False

    # def old_list_things_at(self, location, tclass=Thing):
    #     "Return all things exactly at a given location."
    #     return [thing for thing in self.things
    #             if thing.location == location
    #             and isinstance(thing, tclass)]

    def get_things(self, tclass=Thing):
        "Return all things exactly at a given location."
        rlist = []
        for thing in self.things:
            if not isinstance(thing, tclass):
                continue
            rlist.append(thing)
        return rlist

    def list_things_at(self, location, tclass=Thing):
        "Return all things exactly at a given location."
        rlist = []
        for thing in self.things:
            if thing.location != location:
                continue
            if not isinstance(thing, tclass):
                continue
            rlist.append(thing)
        return rlist

    def some_things_at(self, location, tclass=Thing):
        """Return true if at least one of the things at location
        is an instance of class tclass (or a subclass)."""
        return self.list_things_at(location, tclass) != []

    def add_thing(self, thing, location=None):
        """Add a thing to the environment, setting its location. For
        convenience, if thing is an agent program we make a new agent
        for it. (Shouldn't need to override this."""
        if not isinstance(thing, Thing):
            thing = Agent(thing)
        assert thing not in self.things, "Don't add the same thing twice"
        thing.location = location if location is not None else self.default_location(thing)
        self.things.append(thing)
        if isinstance(thing, Agent):
            thing.performance = 0
            self.agents.append(thing)

    def delete_thing(self, thing):
        """Remove a thing from the environment."""
        try:
            self.things.remove(thing)
        except ValueError as e:
            print(e)
            print("  in Environment delete_thing")
            print("  Thing to be removed: {} at {}" .format(thing, thing.location))
            print("  from list: {}" .format([(thing, thing.location) for thing in self.things]))
        if thing in self.agents:
            self.agents.remove(thing)

class Direction():
    '''A direction class for agents that want to move in a 2D plane
        Usage:
            d = Direction("Down")
            To change directions:
            d = d + "right" or d = d + Direction.R #Both do the same thing
            Note that the argument to __add__ must be a string and not a Direction object.
            Also, it (the argument) can only be right or left. '''

    R = "right"
    L = "left"
    U = "up"
    D = "down"

    def __init__(self, direction):
        self.direction = direction

    def __add__(self, heading):
        if self.direction == self.R:
            return{
                self.R: Direction(self.D),
                self.L: Direction(self.U),
            }.get(heading, None)
        elif self.direction == self.L:
            return{
                self.R: Direction(self.U),
                self.L: Direction(self.L),
            }.get(heading, None)
        elif self.direction == self.U:
            return{
                self.R: Direction(self.R),
                self.L: Direction(self.L),
            }.get(heading, None)
        elif self.direction == self.D:
            return{
                self.R: Direction(self.L),
                self.L: Direction(self.R),
            }.get(heading, None)

    def move_forward(self, from_location):
        x, y = from_location
        if self.direction == self.R:
            return (x+1, y)
        elif self.direction == self.L:
            return (x-1, y)
        elif self.direction == self.U:
            return (x, y-1)
        elif self.direction == self.D:
            return (x, y+1)

class XYEnvironment(Environment):

    """This class is for environments on a 2D plane, with locations
    labelled by (x, y) points, either discrete or continuous.

    Agents perceive things within a radius.  Each agent in the
    environment has a .location slot which should be a location such
    as (0, 1), and a .holding slot, which should be a list of things
    that are held."""

    def __init__(self, width=10, height=10):
        super(XYEnvironment, self).__init__()

        self.width = width
        self.height = height
        self.observers = []
        # Sets iteration start and end (no walls).
        self.x_start, self.y_start = (0, 0)
        self.x_end, self.y_end = (self.width, self.height)

    perceptible_distance = 1

    def things_near(self, location, radius=None):
        "Return all things within radius of location."
        if radius is None:
            radius = self.perceptible_distance
        radius2 = radius * radius
        return [(thing, radius2 - distance2(location, thing.location)) for thing in self.things
                if distance2(location, thing.location) <= radius2]

    def percept(self, agent):
        '''By default, agent perceives things within a default radius.'''
        return self.things_near(agent.location)

    def execute_action(self, agent, action):
        agent.bump = False
        if action == 'Right':
            x, y = agent.location
            agent.bump = self.move_to(agent, (x+1, y))
        elif action == 'Left':
            x, y = agent.location
            agent.bump = self.move_to(agent, (x-1, y))
        elif action == 'Up':
            x, y = agent.location
            agent.bump = self.move_to(agent, (x, y-1))
        elif action == 'Down':
            x, y = agent.location
            agent.bump = self.move_to(agent, (x, y+1))
        elif action == 'TurnRight':
            agent.direction = agent.direction + Direction.R
        elif action == 'TurnLeft':
            agent.direction = agent.direction + Direction.L
        elif action == 'Forward':
            agent.bump = self.move_to(agent, agent.direction.move_forward(agent.location))
#         elif action == 'Grab':
#             things = [thing for thing in self.list_things_at(agent.location)
#                     if agent.can_grab(thing)]
#             if things:
#                 agent.holding.append(things[0])
        elif action == 'Release':
            if agent.holding:
                agent.holding.pop()

    def default_location(self, thing):
        return (random.choice(self.width), random.choice(self.height))

    def move_to(self, thing, destination):
        '''Move a thing to a new location. Returns True on success or False if there is an Obstacle
            If thing is grabbing anything, they move with him '''
        thing.bump = self.some_things_at(destination, Obstacle)
        if not thing.bump:
            thing.location = destination
            for o in self.observers:
                o.thing_moved(thing)
            for t in thing.holding:
                self.delete_thing(t)
                self.add_thing(t, destination)
                t.location = destination
        return thing.bump

    def add_thing(self, thing, location=(1, 1)):
        super(XYEnvironment, self).add_thing(thing, location)
        thing.holding = []
        thing.held = None
        for obs in self.observers:
            obs.thing_added(thing)

    def add_thing(self, thing, location=(1, 1), exclude_duplicate_class_items=False):
        '''Adds things to the world.
            If (exclude_duplicate_class_items) then the item won't be added if the location
            has at least one item of the same class'''
        if (self.is_inbounds(location)):
            if (exclude_duplicate_class_items and
                any(isinstance(t, thing.__class__) for t in self.list_things_at(location))):
                    return
            super(XYEnvironment, self).add_thing(thing, location)
        else:
            pass

    def is_inbounds(self, location):
        '''Checks to make sure that the location is inbounds (within walls if we have walls)'''
        x,y = location
        # this works, but I had trouble debugging it:
        # return not (x < self.x_start or x >= self.x_end or y < self.y_start or y >= self.y_end)
        # so I took it apart:
        if x < self.x_start:
            return False
        if x >= self.x_end:
            return False
        if y < self.y_start:
            return False
        if y >= self.y_end:
            return False
        return True

    def random_location_inbounds(self, exclude=None):
        '''Returns a random location that is inbounds (within walls if we have walls)'''
        location = (random.randint(self.x_start, self.x_end - 1),
                    random.randint(self.y_start, self.y_end - 1))
        if exclude is not None:
            while(location == exclude):
                location = (random.randint(self.x_start, self.x_end - 1),
                            random.randint(self.y_start, self.y_end - 1))
        return location

    def delete_thing(self, thing):
        '''Deletes thing, and everything it is holding (if thing is an agent)'''
        if isinstance(thing, Agent):
            for obj in thing.holding:
                super(XYEnvironment, self).delete_thing(obj)
                for obs in self.observers:
                    obs.thing_deleted(obj)

        super(XYEnvironment, self).delete_thing(thing)
        for obs in self.observers:
            obs.thing_deleted(thing)

    def add_walls(self):
        '''Put walls around the entire perimeter of the grid.'''
        for x in range(self.width):
            self.add_thing(Wall(), (x, 0))
            self.add_thing(Wall(), (x, self.height - 1))
        for y in range(1, self.height-1):
            self.add_thing(Wall(), (0, y))
            self.add_thing(Wall(), (self.width - 1, y))

        # Updates iteration start and end (with walls).
        self.x_start, self.y_start = (1, 1)
        self.x_end, self.y_end = (self.width - 1, self.height - 1)

    def scatter_things(self, type, prob=0.5):
        '''Scatter things around the environment.'''
        for x in range(self.x_start, self.x_end):
            for y in range(self.x_start, self.x_end):
                if random.uniform(0, 1) < prob:
                    self.add_thing(type(), (x, y))

        # Updates iteration start and end (with walls).
        self.x_start, self.y_start = (1, 1)
        self.x_end, self.y_end = (self.width - 1, self.height - 1)

    def add_observer(self, observer):
        """Adds an observer to the list of observers.
        An observer is typically an EnvGUI.

        Each observer is notified of changes in move_to and add_thing,
        by calling the observer's methods thing_moved(thing)
        and thing_added(thing, loc)."""
        self.observers.append(observer)

    # def turn_heading(self, heading, inc):
    #     "Return the heading to the left (inc=+1) or right (inc=-1) of heading."
    #     return turn_heading(heading, inc)

class Obstacle(Thing):

    """Something that can cause a bump, preventing an agent from
    moving into the same square it's in."""
    pass

class Wall(Obstacle):
    pass

# ______________________________________________________________________________
# Continuous environment

class ContinuousWorld(Environment):
    """ Model for Continuous World. """
    def __init__(self, width=10, height=10):
        super(ContinuousWorld, self).__init__()
        self.width = width
        self.height = height

    def add_obstacle(self, coordinates):
        self.things.append(PolygonObstacle(coordinates))

class PolygonObstacle(Obstacle):
    def __init__(self, coordinates):
        """ Coordinates is a list of tuples. """
        super(PolygonObstacle, self).__init__()
        self.coordinates = coordinates

# ______________________________________________________________________________

def compare_agents(EnvFactory, AgentFactories, n=10, steps=1000):
    """See how well each of several agents do in n instances of an environment.
    Pass in a factory (constructor) for environments, and several for agents.
    Create n instances of the environment, and run each agent in copies of
    each one for steps. Return a list of (agent, average-score) tuples."""
    envs = [EnvFactory() for i in range(n)]
    return [(A, test_agent(A, steps, copy.deepcopy(envs)))
            for A in AgentFactories]

def test_agent(AgentFactory, steps, envs):
    "Return the mean score of running an agent in each of the envs, for steps"
    def score(env):
        agent = AgentFactory()
        env.add_thing(agent)
        env.run(steps)
        return agent.performance
    return mean(map(score, envs))