How to train Multi Agent Collaborative Agents with Reinforcement Learning (CTDE Explained)
Skills:
Agent Foundations90%Multi-Agent Systems90%Tool Use & Function Calling80%Autonomous Workflows80%
In this video, we train Multi-agent Navigation AI agents to collaborate in complex obstacle courses. We learned the basics of creating custom Reinforcement Learning environments, how to design observation spaces, action spaces, and reward spaces, as well as the basics of LCS (local coordinate systems) in agentic systems. We then talk about Actor Critic methods like A2C and PPO, and how to train agents using them.
We discuss two multi-agent RL algorithms, namely Independent PPO (I-PPO) and the more advanced Multi Agent PPO (MA-PPO). MA-PPO is inspired by MA-DDPG, which is a Centralized Training Decentralized Execution (CTDE) RL method. We learn why CTDE methods are great at training multi-agent RL environments and why they can promote cooperative and emergent behaviours in RL agents.
The GitHub repo: https://github.com/avbiswas/navigation-mappo-rl
The longer code explainer video is available for Patreon members:
https://www.patreon.com/posts/multi-agent-rl-145270524
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Relevant videos:
Intro to Reinforcement Learning - https://youtu.be/Qpx6WD0qekQ
GRPO and reasoning LLMs - https://youtu.be/yGkJj_4bjpE
RL Playlist - https://www.youtube.com/playlist?list=PLGXWtN1HUjPfays8_pu4nQOW47Q6pzaGP
Useful papers:
- An Introduction to Centralized Training for Decentralized Execution in Cooperative Multi-Agent Reinforcement Learning (https://arxiv.org/abs/2409.03052)
- PPO paper (https://arxiv.org/pdf/1707.06347)
- MARL in Pytorch (https://docs.pytorch.org/rl/main/tutorials/multiagent_ppo.html)
- MA-DDPG (https://arxiv.org/abs/1706.02275)
Timestamps:
0:00 - Intro
2:17 - Creating RL environments
6:23 - Local Coordinate Sys
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Chapters (3)
Intro
2:17
Creating RL environments
6:23
Local Coordinate Sys
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