Regret Minimization for Partially Observable Deep Reinforcement Learning

ICML 2018 Peter JinKurt KeutzerSergey Levine

Deep reinforcement learning algorithms that estimate state and state-action value functions have been shown to be effective in a variety of challenging domains, including learning control strategies from raw image pixels. However, algorithms that estimate state and state-action value functions typically assume a fully observed state and must compensate for partial observations by using finite length observation histories or recurrent networks... (read more)

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