A Pytorch implementation of the multi agent deep deterministic policy gradients (MADDPG) algorithm

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Deep Learningreinforcement-learningdeep-reinforcement-learningactor-critic-methodsactor-critic-algorithmmulti-agent-reinforcement-learningmaddpg
Overview

Multi-Agent-Deep-Deterministic-Policy-Gradients

A Pytorch implementation of the multi agent deep deterministic policy gradients(MADDPG) algorithm

This is my implementation of the algorithm presented in the paper: Multi Agent Actor Critic for Mixed Cooperative-Competitive Environments. You can find this paper here: https://arxiv.org/pdf/1706.02275.pdf

You will need to install the Multi Agent Particle Environment(MAPE), which you can find here: https://github.com/openai/multiagent-particle-envs

Make sure to create a virtual environment with the dependencies for the MAPE, since they are somewhat out of date. I also recommend running this with PyTorch version 1.4.0, as the latest version (1.8) seems to have an issue with an in place operation I use in the calculation of the critic loss.

It's probably easiest to just clone this repo into the same directory as the MAPE, as the main file requires the make_env function from that package.

The video for this tutorial is found here: https://youtu.be/tZTQ6S9PfkE

Owner
Phil Tabor
Physicist, Machine Learning Engineer
Phil Tabor
GitHub Repository
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