FADQN

Federated Averaging Deep Q-Network

A distributed Reinforcement Learning framework

Installation

• Install Anaconda
• Create environment conda create --name py36 python=3.6
• Activate environment (Windows) conda activate py36
• Activate environment (Ubuntu) source conda activate py36
• Install Python packages

pip install tensorflow
pip install gym
pip install dill
(conda install -y scipy)

Goal for this project

Implement a distributed DQN algorithm according to this specification

How to run

cd baselines/deepq/experiments/
python async_fed_avg.py [--config_file config.ini]
                        [--config DEFAULT]
                         --job_name "worker"
                         --task_index 0
                        [--seed 1]

• config_file is the name of the config file (or path if it's in a different directory).

  • Defaults to config.ini

• config is the section in the config file to override the default values with.

  • Defaults to DEFAULT. Use async for the [async] section and sync for the [sync]

• job_name is the type of job the current client should perform.

  • Defaults to worker. Use only worker or ps for this value

• task_index is the index of the current server's IP in the list for its job (ps or worker).

  • Defaults to 0. Worker 0 will become the chief with extra responsibilities

• seed is the seed for all the randomness in the server.

  • Defaults to 1. The server's task index will be added to this to make sure every server has a unique seed

Files to edit

The asynchronous Cart Pole script is located at

baselines/deepq/experiments/async_fed_avg.py

Most edits will be done there and in the build graph file at

baselines/deepq/build_graph.py