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Distributed Policy Gradient with Variance Reduction in Multi-Agent Reinforcement Learning
This paper studies a distributed policy gradient in collaborative multi-agent reinforcement learning (MARL), where agents over a communication network aim to find the optimal policy to maximize the average of all agents' local returns.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Active Crowd Counting with Limited Supervision
To learn a reliable people counter from crowd images, head center annotations are normally required.
