Variational Recurrent Models for Solving Partially Observable Control Tasks

ICLR 2020 Dongqi HanKenji DoyaJun Tani

In partially observable (PO) environments, deep reinforcement learning (RL) agents often suffer from unsatisfactory performance, since two problems need to be tackled together: how to extract information from the raw observations to solve the task, and how to improve the policy. In this study, we propose an RL algorithm for solving PO tasks... (read more)

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