Artificial Intelligence
多智能体深度强化学习
Multi-Agent Deep Reinforcement LearningA subfield of machine learning that combines reinforcement learning with deep learning to enable multiple agents to learn optimal policies through interaction with an environment and each other. / 一种结合了强化学习与深度学习的机器学习子领域，使多个智能体能够通过与环境和彼此的交互来学习最优策略。
- Knowledge type
- 数学理论
- First proposed or discovered
- Proposer
- Turkey Farm member
- Creator
- 农场主
- Last updated
- 2026-11-14 23:06:00
Sub-knowledge points
Multi-Agent Reinforcement Learning (MARL)Deep Reinforcement Learning (DRL)Actor-Critic MethodsValue Decomposition Networks
Higher-order knowledge points
Main parameters and performance indicators
Agents: The number of independent learning entitiesEnvironment: The dynamic system the agents interact withReward Function: The feedback signal guiding the learning process
Open problems
Scalability with increasing number of agentsCommunication efficiency between agentsTheoretical guarantees of convergence
Related literature
ranked by community starsDistributed Game-Theoretic Learning for Integrated Sensing and Communication in Multi-UAV Systems: A Two-Stage MADRL ApproachOpen source
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1 recordsRecord title: Multi-Agent Deep Reinforcement Learning.
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