Selectively Sharing Experiences Improves Multi-Agent Reinforcement Learning

Neural Information Processing Systems 

We present a novel multi-agent RL approach, Selective Multi-Agent Prioritized Experience Relay, in which agents share with other agents a limited number of transitions they observe during training.

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