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Deep reinforcement learning (RL) has achieved great empirical successes in various domains. However, the large search space of neural networks requires a large amount of data, which makes the current RL algorithms not sample efficient. Motivated by the fact that many environments with continuous state space have smooth transitions, we propose to learn a smooth policy that behaves smoothly with respect to states. We develop a new framework – Smooth Regularized Reinforcement Learning (SR^2L),arXiv:2003.09534v4 fatcat:b5qinalozjhdpg6i6skou57rdq