Sample-efficient policy learning in multi-agent Reinforcement Learning via meta-learning

ICLR 2019 • Jialian Li • Hang Su • Jun Zhu

To gain high rewards in muti-agent scenes, it is sometimes necessary to understand other agents and make corresponding optimal decisions. We can solve these tasks by first building models for other agents and then finding the optimal policy with these models... (read more)

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