Welcome to the tutorials section on Multi-Agent Reinforcement Learning (MARL). Here, we delve into the fascinating world of MARL, where multiple agents interact with each other in an environment to achieve common goals or compete against each other.
Basic Concepts
- Agent: An entity that interacts with the environment and learns from its experiences.
- Environment: The context in which the agents operate, which provides rewards or penalties based on the agents' actions.
- Policy: A set of rules that governs the actions of an agent.
- Value Function: A function that estimates the value of being in a particular state.
- Q-Function: A function that estimates the expected future reward of taking a particular action in a given state.
Tutorials
1. Introduction to Multi-Agent Reinforcement Learning
This tutorial provides a comprehensive overview of the basics of MARL, including the key concepts and terminologies.
2. Multi-Agent Deep Q-Networks (MADDPG)
This tutorial explains the MADDPG algorithm, which combines the DQN architecture with multi-agent reinforcement learning.
3. Multi-Agent Policy Gradient (MAPG)
This tutorial explores the MAPG algorithm, which uses policy gradients to train multiple agents in a collaborative or competitive environment.
4. Multi-Agent Deep Deterministic Policy Gradient (MADDPG)
This tutorial discusses the MADDPG algorithm, which extends the DDPG architecture to handle multi-agent scenarios.
Resources
- Reinforcement Learning Library: A comprehensive library for reinforcement learning algorithms and environments.
- Multi-Agent Reinforcement Learning Papers: A collection of research papers on multi-agent reinforcement learning.
Multi-Agent Reinforcement Learning