Monte-Carlo Tree Search for Policy Optimization

23 Dec 2019Xiaobai MaKatherine Driggs-CampbellZongzhang ZhangMykel J. Kochenderfer

Gradient-based methods are often used for policy optimization in deep reinforcement learning, despite being vulnerable to local optima and saddle points. Although gradient-free methods (e.g., genetic algorithms or evolution strategies) help mitigate these issues, poor initialization and local optima are still concerns in highly nonconvex spaces... (read more)

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