This code is modified from MADDPG and M3DDPG.

Robust Multi-Agent Reinforcement Learning with State Uncertainty

This is the code for implementing the Robust Multi-Agent Actor-Critic (RMAAC) algorithm presented in the paper: Robust Multi-Agent Reinforcement Learning with State Uncertainty. It is configured to be run in conjunction with environments from the Multi-Agent Particle Environments (MPE).

Installation

Known dependencies: Python (3.5.4), OpenAI gym (0.10.5), tensorflow (1.8.0), numpy (1.14.5)

You can use the following commands to configure the environment.

conda create -n rmaac_env python=3.5.4

conda activate rmaac_env

conda install numpy=1.14.5

# conda install -c anaconda tensorflow-gpu

conda install tensorflow

# conda install gym=0.10.5

pip install gym==0.10.5

Multi-Agent Particle Environments

We demonstrate here how the code can be used in conjunction with the Multi-Agent Particle Environments (MPE).

Download and install the MPE code here by following the README.
Ensure that multiagent-particle-envs has been added to your PYTHONPATH (e.g. in ~/.bashrc or ~/.bash_profile).
To run the code, cd into the experiments directory and run train.py:

python train.py --scenario simple

You can replace simple with any environment in the MPE you'd like to run.

Command-line options

Environment options

--scenario: defines which environment in the MPE is to be used (default: "simple")
--max-episode-len maximum length of each episode for the environment (default: 25)
--num-episodes total number of training episodes (default: 60000)
--num-adversaries number of adversaries in the game (default: 0)

Core training parameters

--lr: learning rate for agents (default: 1e-2)
--lr-adv: learning rate for state perturbation adversaries(default: 1e-2)
--gamma: discount factor (default: 0.95)
--batch-size: batch size (default: 1024)
--num-units: number of units in the MLP (default: 64)
--noise-type: noise format (default: Linear)
--noise-variance: variance of gaussian noise (default: 1)
--constraint-epsilon: the constraint parameter (default: 0.5)

Checkpointing

--exp-name: name of the experiment, used as the file name to save all results (default: None)
--save-dir: directory where intermediate training results and model will be saved (default: "/tmp/policy/")
--save-rate: model is saved every time this number of episodes has been completed (default: 1000)
--load-dir: directory where training state and model are loaded from (default: "")

Evaluation

--restore: restores previous training state stored in load-dir (or in save-dir if no load-dir has been provided), and continues training (default: False)
--display: displays to the screen the trained policy stored in load-dir (or in save-dir if no load-dir has been provided), but does not continue training (default: False)
--benchmark: runs benchmarking evaluations on saved policy, saves results to benchmark-dir folder (default: False)
--benchmark-iters: number of iterations to run benchmarking for (default: 100000)
--benchmark-dir: directory where benchmarking data is saved (default: "./benchmark_files/")
--plots-dir: directory where training curves are saved (default: "./learning_curves/")

Paper citation

If you used this code for your experiments or found it helpful, consider citing the following paper:

@article{
he2023robust,
title={Robust Multi-Agent Reinforcement Learning with State Uncertainty},
author={Sihong He, Songyang Han, Sanbao Su, Shuo Han, Shaofeng Zou, and Fei Miao},
journal={Transactions on Machine Learning Research},
year={2023},
url={https://openreview.net/forum?id=CqTkapZ6H9}
}

sihongho/Robust_MARL_with_State_Uncertainty