rl baselines zoo
Basic Information
RL Baselines Zoo is a repository that collects and shares a large set of pre-trained reinforcement learning agents and the related training assets. The archive advertises 100+ pre-trained RL agents built using the Stable Baselines library and states that training and hyperparameter optimization resources are included. The project is intended as a practical reference and resource for people who want ready-made RL policies, to reproduce experiments, or to use baseline agents for comparisons or as starting points for further research or application development. The repository has been archived and is read-only as noted in the README.
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App Details
Features
A large collection of over 100 pre-trained reinforcement learning agents provided with the repository. Agents are implemented using the Stable Baselines framework which defines the algorithms and runtime. The repository includes training code and resources for hyperparameter optimization so users can retrain or tune agents. Pre-trained agent files are provided to allow immediate evaluation or reuse without retraining from scratch. The project serves as a consolidated baseline suite for RL experiments and comparisons and is distributed in an archived, read-only state.
Use Cases
This repository helps researchers and developers by providing a ready set of baseline RL agents that can be evaluated, compared, or fine-tuned, saving time compared with training from scratch. The inclusion of training code and hyperparameter optimization artifacts supports reproducibility and allows users to adapt or extend experiments. Practitioners can use the pre-trained models to prototype applications, validate algorithmic choices, or benchmark new methods against established implementations. Students and newcomers can study concrete examples of Stable Baselines usage and training workflows. Note that the project is archived and intended primarily as a reference and historical resource.
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