# Regularization of Soft Actor-Critic Algorithms with Automatic Temperature Adjustment

arXiv: https://arxiv.org/abs/2305.11831 · HTML: https://mlsanity.com/paper/2305.11831/

## 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.

## Builds on (most cited first, 3 of 3)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [Learning to Walk via Deep Reinforcement Learning](https://mlsanity.com/paper/1812.11103/) (2018)

## 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.