SAClearn

SAClearn is a software library designed to support reinforcement learning research and development centered on the Soft Actor-Critic (SAC) family of algorithms. It provides reference implementations of SAC and common variants, along with tooling to configure experiments, train agents, and evaluate performance on continuous control tasks. The project aims to improve reproducibility and comparability of results by offering standardized training loops, evaluation protocols, and experiment logging.

The architecture is organized around modular components, including an actor network that represents the stochastic policy, one or more critic networks for estimating action values, and target networks used to stabilize learning. A central training loop coordinates data collection from the environment, experience replay buffering, and gradient-based optimization for both actor and critic. Optional features commonly associated with SAC implementations—such as automatic entropy tuning, multiple critic ensembles, and prioritized replay—are designed to be pluggable.

SAClearn emphasizes interoperability with common Python-based machine learning stacks and environments. It typically supports running experiments with standard reinforcement learning benchmarks and Gym-compatible environments, and provides utilities for policy evaluation, hyperparameter sweeps, and result visualization. The project is generally released under an open-source license and maintained through a public repository with contribution guidelines and issue tracking. Documentation and tutorials are commonly provided to assist new users in getting started, reproducing published results, and extending the framework with custom components. Related topics include Soft Actor-Critic, deep reinforcement learning libraries, and experiment management tools.