Multi-agent reinforcement learning (MARL) is a subfield of machine learning that focuses on training multiple agents to achieve a common goal while interacting with each other in an environment. This page provides an overview of resources related to MARL.
Key Concepts
- Reinforcement Learning (RL): A type of machine learning where an agent learns to make decisions by performing actions in an environment to achieve a goal.
- Multi-Agent Systems: A system composed of multiple interacting agents that can make decisions based on their environment and the actions of other agents.
Resources
Online Courses
- Deep Learning Specialization by Andrew Ng: This series of courses covers various aspects of deep learning, including reinforcement learning.
- Multi-Agent Reinforcement Learning with Python: This course provides an introduction to multi-agent reinforcement learning and its applications.
Books
- Multi-Agent Systems: A Modern Approach: A comprehensive book on multi-agent systems, covering both theoretical and practical aspects.
- Reinforcement Learning: An Introduction: A book that provides an introduction to reinforcement learning, including multi-agent systems.
Research Papers
- Algorithms for Multi-Agent Reinforcement Learning: This paper provides an overview of algorithms for multi-agent reinforcement learning.
- Cooperative Multi-Agent Reinforcement Learning: This paper focuses on cooperative multi-agent reinforcement learning, where agents work together to achieve a common goal.
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Additional Resources
For more information on multi-agent reinforcement learning, you can visit the following links:
Multi-Agent Reinforcement Learning