Phil Tabor

Phil Tabor

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Multi Agent Deep Deterministic Policy Gradients (MADDPG) in PyTorch

Multi agent deep deterministic policy gradients is one of the first successful algorithms for multi agent artificial intelligence. Cooperation and competition among AI agents is going to be critical as applications of deep learning expand in our daily lives. In this tutorial, we are going to read through the paper together and then code up the entire multi agent actor critic algorithm from scratch in the Pytorch framework.

The main innovation of this algorithm is the use of centralized execution and decentralized training. In brief, we’re going to give each agent’s critic network access to the observations and actions of all the agents in the simulation. The actor networks will only have access to their own perspective, hence the centralized execution.

We are going to use Open AI’s multi agent particle environment for training and testing our agents. I’ll show you how to get it from github and install the requirements in a virtual environment. We’ll cover some of the ways in which the new environments differ from the classic Open AI gym environments, and then we’re off to coding our agents.

https://youtu.be/tZTQ6S9PfkE

#reinforcement-learning #pytorch #python #deep-learning #machine-learning #artificial-intelligence

 Multi Agent Deep Deterministic Policy Gradients (MADDPG) in PyTorch