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Learning to Communicate with Deep Multi-Agent Reinforcement Learning

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


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.


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

Results for switch game

DIAL vs. RIAL reward curves

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

More info

More generally, 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 -

JSON configuration files passed to 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 to graph results output by 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>


    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},


Code licensed under the Apache License v2.0