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Actor Loss of Soft Actor Critic Explained

arXiv 2112.15568 ↗First posted 31 Dec 2021cs.LGcited by 0

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Abstract (arXiv)

This technical report is devoted to explaining how the actor loss of soft actor critic is obtained, as well as the associated gradient estimate. It gives the necessary mathematical background to derive all the presented equations, from the theoretical actor loss to the one implemented in practice. This necessitates a comparison of the reparameterization trick used in soft actor critic with the nabla log trick, which leads to open questions regarding the most efficient method to use.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 8 references that are in the map. First posted on arXiv 31 Dec 2021.

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5 of 8 references.

References · 5

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  2. 2015Prioritized Experience Replaycited by 1,070
  3. 2020Munchausen Reinforcement Learningcited by 68
  4. 2020Leverage the Average: an Analysis of KL Regularization in RLcited by 33
  5. 2019Boosting Soft Actor-Critic: Emphasizing Recent Experience without Forgetting the Pastcited by 16

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