Multimed Tools Appl https://doi.org/10.1007/s11042-018-6098-y A monotonic policy optimization algorithm for high-dimensional continuous control problem in 3D MuJoCo 1 1 Qunyong Yuan & Nanfeng Xiao Received: 25 January 2018 /Revised: 13 March 2018 /Accepted: 3 May 2018 Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract One challenge in applying reinforcement learning with nonlinear function approximator to high- dimensional continuous control problems is that the update policy pro- duced by the many existed algorithms may fail to improve policy performance or even causes a serious degradation of the policy performance. To address this challenge, this paper proposes a new lower bound on the policy improvement where an average policy divergence on state space is penalized. To the best of our knowledge, this is currently the best result about the lower bound on the policy improvement. Optimizing directly the lower bound on the policy improvement is very difficult, because it demands for high computational overhead. According to the ideal of the trust region policy optimization (TRPO), this paper also presents a monotonic policy optimization algorithm, which is based on the new lower bound on the policy improvement introduced in this paper, it can generate a sequence of monotonically improving policies,
Multimedia Tools and Applications – Springer Journals
Published: Jun 4, 2018
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