Learning to Communicate with Deep Multi-Agent Reinforcement Learning

This is a PyTorch implementation of the original Lua code release.

Overview

This codebase implements two approaches to learning discrete communication protocols for playing collaborative games: Reinforced Inter-Agent Learning (RIAL), in which agents learn a factorized deep Q-learning policy across game actions and messages, and Differentiable Inter-Agent Learning (DIAL), in which the message vectors are directly learned by backpropagating errors through a noisy communication channel during training, and discretized to binary vectors during test time. While RIAL and DIAL share the same individual network architecture, one would expect learning to be more efficient under DIAL, which directly backpropagates downstream errors during training, a fact that is verified in comparing the performance of the two approaches.

Execution

$ virtualenv .venv
$ source .venv/bin/activate
$ pip install -r requirements.txt
$ python main.py -c config/switch_3_dial.json

Results for switch game

This chart was generated by plotting an exponentially-weighted average across 20 trials for each curve.

More info

More generally, main.py takes multiple arguments:

Arg	Short	Description	Required?
--config_path	-c	path to JSON configuration file	✅
--results_path	-r	path to directory in which to save results per trial (as csv)	-
--ntrials	-n	number of trials to run	-
--start_index	-s	start-index used as suffix in result filenames	-
--verbose	-v	prints results per training epoch to stdout if set	-

Configuration

JSON configuration files passed to main.py should consist of the following key-value pairs:

Key	Description	Type
game	name of the game, e.g. "switch"	string
game_nagents	number of agents	int
game_action_space	number of valid game actions	int
game_comm_limited	true if only some agents can communicate at each step	bool
game_comm_bits	number of bits per message	int
game_comm_sigma	standard deviation of Gaussian noise applied by DRU	float
game_comm_hard	true if use hard discretization, soft approximation otherwise	bool
nsteps	maximum number of game steps	int
gamma	reward discount factor for Q-learning	float
model_dial	true if agents should use DIAL	bool
model_comm_narrow	true if DRU should use sigmoid for regularization, softmax otherwise	bool
model_target	true if learning should use a target Q-network	bool
model_bn	true if learning should use batch normalization	bool
model_know_share	true if agents should share parameters	bool
model_action_aware	true if each agent should know their last action	bool
model_rnn_size	dimension of rnn hidden state	int
bs	batch size of episodes, run in parallel per epoch	int
learningrate	learning rate for optimizer (RMSProp)	float
momentum	momentum for optimizer (RMSProp)	float
eps	exploration rate for epsilon-greedy exploration	float
nepisodes	number of epochs, each consisting of parallel episodes	int
step_test	perform a test episode every this many steps	int
step_target	update target network every this many steps	int

Visualizing results

You can use analyze_results.py to graph results output by main.py. This script will plot the average results across all csv files per path specified after -r. Further, -a can take an alpha value to plot results as exponentially-weighted moving averages, and -l takes an optional list of labels corresponding to the paths.

$ python util/analyze_results -r <paths to results> -a <weight for EWMA>

Bibtex

@inproceedings{foerster2016learning,
    title={Learning to communicate with deep multi-agent reinforcement learning},
    author={Foerster, Jakob and Assael, Yannis M and de Freitas, Nando and Whiteson, Shimon},
    booktitle={Advances in Neural Information Processing Systems},
    pages={2137--2145},
    year={2016} 
}

License

Code licensed under the Apache License v2.0