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.
an effective policy increases with task complexity. Off-policy algorithms aim to reuse past experience. This is not directly feasible with conventional policy gradient formula-tions, but is relatively straightforward for Q-learning based methods (Mnih et al.,2015). Unfortunately, the combina-tion of off-policy learning and high-dimensional ...
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