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Regularization of Soft Actor-Critic Algorithms with Automatic Temperature Adjustment

arXiv 2305.11831 ↗First posted 19 May 2023cs.LG cs.AIcited by 0

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Abstract (arXiv)

This work presents a comprehensive analysis to regularize the Soft Actor-Critic (SAC) algorithm with automatic temperature adjustment. The the policy evaluation, the policy improvement and the temperature adjustment are reformulated, addressing certain modification and enhancing the clarity of the original theory in a more explicit manner.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 3 references that are in the map. First posted on arXiv 19 May 2023.

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References · 3

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,240
  3. 2018Learning to Walk via Deep Reinforcement Learningcited by 158

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