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This release adds a new algorithm: Soft Actor-Critic (SAC). Soft Actor-Critic -implement the original paper: "Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor" https://arxiv.org/abs/1801.01290 #398 implement the improvement of SAC paper: "Soft Actor-Critic Algorithms and Applications" https://arxiv.org/abs/1812.05905 #399 extend SAC to work directly for discrete environment using GumbelSoftmax distribution (custom) Roboschool (continuous control) Benchmark Note that the Roboschool reward scales are different from MuJoCo's. Env. \ Alg. SAC RoboschoolAnt 2451.55 <details><summary><i>graph</i></summary><img src="https://user-images.githubusercontent.com/8209263/62837481-c1eead80-bc24-11e9-913e-7685d64ecf87.png"></details> RoboschoolHalfCheetah 2004.27 <details><summary><i>graph</i></summary><img src="https://user-images.githubusercontent.com/8209263/62837485-daf75e80-bc24-11e9-8fba-279802ccdd1d.png"></details> RoboschoolHopper 2090.52 <details><summary><i>graph</i></summary><img src="https://user-images.githubusercontent.com/8209263/62837491-e8144d80-bc24-11e9-9d06-27a35b4aacca.png"></details> RoboschoolWalker2d 1711.92 <details><summary><i>graph</i></summary><img src="https://user-images.githubusercontent.com/8209263/62837495-f2364c00-bc24-11e9-8bdc-fa88831c227b.png"></details> LunarLander (discrete control) Benchmark Trial graph Moving average
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