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. In this framework, the actor aims to maxi-mize expected reward while also maximizing en-tropy.
by acquiring diverse behaviors. Prior work has proposed model-free deep RL algorithms that perform on-policy learn-ing with entropy maximization (O’Donoghue et al.,2016), as well as off-policy methods based on soft Q-learning and its variants (Schulman et al.,2017a;Nachum et al.,2017a; Haarnoja et al.,2017). However, the on-policy variants suf-
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