Multi-Agent Reinforcement Learning
Machine Learning · Further Education · Reinforcement Learning
Multi-Agent Reinforcement Learning (MARL) extends reinforcement learning to scenarios with multiple agents. This unit introduces the fundamental concepts, algorithms, and applications of MARL, enabling learners to understand how agents can cooperate and compete in dynamic environments.
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Das gehört dazu
- Introduction to Multi-Agent Systems
- Reinforcement Learning Fundamentals
- Cooperative Multi-Agent Reinforcement Learning
- Competitive Multi-Agent Reinforcement Learning
- Game Theory and MARL
- Communication in MARL
- Decentralized MARL
- Applications of MARL
Stichworte
- Multi-Agent Systems
- Reinforcement Learning
- Game Theory
- Cooperative Learning
- Competitive Learning
- Nash Equilibrium
- Q-Learning
- Policy Gradient Methods
- Decentralized Learning