Transcription of Soft Actor-Critic: Off-Policy Maximum Entropy Deep ...
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Soft Actor-Critic: Off-Policy Maximum Entropy Deep ...
Soft Actor-Critic: Off-Policy Maximum Entropy Deep ReinforcementLearning with a Stochastic ActorTuomas Haarnoja1 Aurick Zhou1 Pieter Abbeel1 Sergey Levine1 AbstractModel-free deep reinforcement learning (RL) al-gorithms have been demonstrated on a range ofchallenging decision making and control , these methods typically suffer from twomajor challenges: very high sample complexityand brittle convergence properties, which necessi-tate meticulous hyperparameter tuning. Both ofthese challenges severely limit the applicabilityof such methods to complex, real-world this paper, we propose soft actor-critic, an Off-Policy actor-critic deep RL algorithm based on themaximum Entropy reinforcement learning frame-work.
Maximum entropy reinforcement learning optimizes poli-cies to maximize both the expected return and the ex-pected entropy of the policy. This framework has been used in many contexts, from inverse reinforcement learn-ing (Ziebart et al.,2008) to optimal control (Todorov,2008; Toussaint,2009;Rawlik et al.,2012). In guided policy
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