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// ♞ Reinforcement Learning

Soft Actor-Critic (SAC)

Off-policy actor-critic maximizing both return and entropy.

软演员-评论家 SAC 2018 Advanced extended

Original paper ↓ What to learn first →

IN PLAIN WORDS

Encourages exploration via an entropy bonus, with replay and twin Q networks — stable and sample-efficient for continuous control.

Used in: Robot and continuous control

TIMELINE · prerequisites → this → what builds on it

2013 Deep Q-Networ…2017 Proximal Poli…2018 Soft Actor-Critic…2019 Dreamer (Late…

01Original paper

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, et al. · 2018

arXiv ↗ ✓ Verified · arXiv API

02Where it sits

Learn the left column first; the right column builds on this one.

Learn first

Soft Actor-Critic (SAC)

Then you can learn

Proximal Policy…Deep Q-Network …Dreamer (Latent… Soft Actor-Critic (…

Left: prerequisites · right: builds on this · dashed: related. Full map →

03Learning resources

Official docs, explainers, courses and reference code. All links checked.

Official docs

04Where the course teaches it

U33 Reinforcement Learning Basics MDPs, Q-learning, DQN, policy gradients, PPO and AlphaZero — the prerequisites for post-training and reasoning models. 14 h12 algorithms4 papers
For AI / data

Data updated: AI algorithms & curriculum(UTC+8)