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The Point to Which Soft Actor-Critic Converges

arXiv 2303.01240 ↗First posted 1 Mar 2023cs.LGcited by 0

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Abstract (arXiv)

Soft actor-critic is a successful successor over soft Q-learning. While lived under maximum entropy framework, their relationship is still unclear. In this paper, we prove that in the limit they converge to the same solution. This is appealing since it translates the optimization from an arduous to an easier way. The same justification can also be applied to other regularizers such as KL divergence.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 3 references that are in the map. First posted on arXiv 1 Mar 2023.

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2 of 3 references.

References · 2

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2017Reinforcement Learning with Deep Energy-Based Policiescited by 669

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