Overview
This week focuses on Multi-Agent Systems and Reinforcement Learning, exploring how multiple AI agents can interact, learn, and solve complex problems collectively. We’ll cover fundamental concepts, algorithms, and applications in this exciting field of AI.
Instructor
Yaodong Yang, PKU
Topics Covered
- Introduction to Multi-Agent Systems
- Fundamentals of Reinforcement Learning
- Ray framework for distributed computing
- Multi-agent concepts and applications
- Introduction to distributed learning
Assignments
Practice Assignment:
- Implement the PPO algorithm and test it on the CartPole environment.
- Implement the MADDPG algorithm and test it in the Speaker Listener environment.
Written Assignment:
- Implement the PPO algorithm and test it on the CartPole environment.
Assignment: Multi-Agent Systems
Additional Resources
- Multi-Agent Reinforcement Learning by Stefano V. Albrecht, Filippos Christianos, and Lukas Schäfer
- Ray Documentation
- Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto
- Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations by Yoav Shoham and Kevin Leyton-Brown
Notes
- This module builds on concepts from previous weeks, particularly in machine learning.
- For the practice assignment, focus on setting up a working multi-agent environment and implementing a basic RL algorithm.
- In your written assignment, consider both current applications and potential future developments in Multi-Agent Systems.
- As always, document your code thoroughly and use version control (Git) for your project.
- Submit your assignments on GitHub Classroom.