This tutorial provides an introduction to Multi-Agent Reinforcement Learning (MARL) within the context of games. MARL is a branch of machine learning that focuses on the interaction between multiple intelligent agents in an environment.
Overview
In this section, we'll cover the basics of MARL, its applications in games, and some popular algorithms used in the field.
Basics of MARL
- Definition: MARL is a type of reinforcement learning where multiple agents learn to make decisions together.
- Applications: MARL is used in various fields, including gaming, robotics, and economics.
- Challenges: Synchronizing the actions of multiple agents while ensuring fairness and cooperation.
MARL in Games
- Simulations: Many games provide a platform for testing and developing MARL algorithms.
- Applications: MARL in games can lead to more realistic and engaging gameplay experiences.
Algorithms
- Q-Learning: A popular algorithm for single-agent reinforcement learning, which can be adapted for MARL.
- Deep Q-Network (DQN): An extension of Q-Learning that uses deep neural networks to approximate the Q-values.
- Proximal Policy Optimization (PPO): An algorithm that balances exploration and exploitation, often used in complex environments.
Resources
For further reading on MARL, check out the following resources: