haarnoja/sac

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1301 stars · 252 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

The original reference implementation of Soft Actor-Critic (SAC), a deep reinforcement learning algorithm for training maximum entropy policies in continuous action domains, built on TensorFlow. The repository is no longer maintained and points users to the successor softlearning package.

Use cases

• train maximum entropy policies for continuous control tasks
• reproduce results from the SAC ICML 2018 paper
• run reinforcement learning experiments in Mujoco environments
• learn diverse skills with DIAYN
• study an off-policy actor-critic algorithm implementation
• benchmark continuous-domain RL agents

When to choose

• you need the exact original TensorFlow SAC implementation for research reproduction
• you want to extend or study the reference code behind the SAC paper
• you need DIAYN-based diverse skill learning on top of SAC

When to avoid

• you want an actively maintained SAC implementation - use softlearning or a PyTorch alternative like rlkit
• you prefer PyTorch over TensorFlow
• you need modern dependency versions or recent bug fixes
• you want to avoid the legacy rllab and Mujoco setup burden

Facets

library · maturity abandoned

reinforcement-learning machine-learning deep-learning reinforcement-learning machine-learning robotics python soft-actor-critic maximum-entropy-policy tensorflow mujoco continuous-control off-policy-learning diayn superseded-by-softlearning docker linux macos

Member repositories

For agents

markdown · JSON · MCP: product_card(name="haarnoja/sac")