pytorch-maddpg

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#+TITLE: An implementation of MADDPG #+AUTHOR: xuehy #+EMAIL: [email protected] #+STARTUP: content

• 1. Introduction

This is a pytorch implementation of [[https://arxiv.org/abs/1706.02275][multi-agent deep deterministic policy gradient algorithm]].

The experimental environment is a modified version of Waterworld based on [[https://github.com/sisl/MADRL][MADRL]].

• 2. Environment

The main features (different from MADRL) of the modified Waterworld environment are:

• evaders and poisons now bounce at the wall obeying physical rules
• sizes of the evaders, pursuers and poisons are now the same so that random actions will lead to average rewards around 0.
• need exactly n_coop agents to catch food.

• 3. Dependency

• [[https://github.com/pytorch/pytorch][pytorch]]
• [[https://github.com/facebookresearch/visdom][visdom]]
• =python==3.6.1= (recommend using the anaconda/miniconda)
• if you need to render the environments, =opencv= is required

• 4. Install

• Install [[https://github.com/sisl/MADRL][MADRL]].
• Replace the =madrl_environments/pursuit= directory with the one in this repo.
• =python main.py=

if scene rendering is enabled, recommend to install =opencv= through [[https://github.com/conda-forge/opencv-feedstock][conda-forge]].

• 5. Results

** two agents, cooperation = 2 The two agents need to cooperate to achieve the food for reward 10.

[[PNG/demo.gif]]

[[PNG/3.png]]

the average

[[PNG/4.png]]

** one agent, cooperation = 1

[[PNG/newplot.png]]

• 6. TODO

• reproduce the experiments in the paper with competitive environments.