Regularization Matters in Policy Optimization -- An Empirical Study on Continuous Control

21 Oct 2019 Zhuang Liu Xuanlin Li Bingyi Kang Trevor Darrell

Deep Reinforcement Learning (Deep RL) has been receiving increasingly more attention thanks to its encouraging performance on a variety of control tasks. Yet, conventional regularization techniques in training neural networks (e.g., $L_2$ regularization, dropout) have been largely ignored in RL methods, possibly because agents are typically trained and evaluated in the same environment, and because the deep RL community focuses more on high-level algorithm designs... (read more)

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METHOD TYPE
Entropy Regularization
Regularization