Provably Efficient Exploration in Policy Optimization

ICML 2020 Qi CaiZhuoran YangChi JinZhaoran Wang

While policy-based reinforcement learning (RL) achieves tremendous successes in practice, it is significantly less understood in theory, especially compared with value-based RL. In particular, it remains elusive how to design a provably efficient policy optimization algorithm that incorporates exploration... (read more)

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