On Improving Deep Reinforcement Learning for POMDPs

17 Apr 2018Pengfei ZhuXin LiPascal PoupartGuanghui Miao

Deep Reinforcement Learning (RL) recently emerged as one of the most competitive approaches for learning in sequential decision making problems with fully observable environments, e.g., computer Go. However, very little work has been done in deep RL to handle partially observable environments... (read more)

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