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Sample and Communication Efficient Fully Decentralized MARL Policy Evaluation via a New Approach: Local TD update
This leads to an interesting open question: Can the local TD-update approach entail low sample and communication complexities?
Finite-Time Convergence and Sample Complexity of Multi-Agent Actor-Critic Reinforcement Learning with Average Reward
In this paper, we establish the first finite-time convergence result of the actor-critic algorithm for fully decentralized multi-agent reinforcement learning (MARL) problems with average reward.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
