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A pytorch implementation of MADDPG (multi-agent deep deterministic policy gradient)
| Date | Stars |
|---|---|
| 2026-07-31 | 691 |
| 2026-08-06 | 691 |
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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.
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matched fp:cfe1d0a70925a975, desc:multi-agent, desc:multi agent