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Fully Decentralized Cooperative Multi-Agent Reinforcement Learning: A Survey
Cooperative multi-agent reinforcement learning is a powerful tool to solve many real-world cooperative tasks, but restrictions of real-world applications may require training the agents in a fully decentralized manner.
Decentralized Policy Optimization
In this paper, we propose \textit{decentralized policy optimization} (DPO), a decentralized actor-critic algorithm with monotonic improvement and convergence guarantee.
Multi-Agent Automated Machine Learning
In this paper, we propose multi-agent automated machine learning (MA2ML) with the aim to effectively handle joint optimization of modules in automated machine learning (AutoML).
Multi-Agent Sequential Decision-Making via Communication
Communication helps agents to obtain information about others so that better coordinated behavior can be learned.
MA2QL: A Minimalist Approach to Fully Decentralized Multi-Agent Reinforcement Learning
Decentralized learning has shown great promise for cooperative multi-agent reinforcement learning (MARL).
Divergence-Regularized Multi-Agent Actor-Critic
Though divergence regularization has been proposed to settle this problem, it cannot be trivially applied to cooperative multi-agent reinforcement learning (MARL).
Multi-agent Reinforcement Learning
reinforcement-learning
+2
