rl baselines3 zoo
Basic Information
rl-baselines3-zoo is a training framework designed to work with Stable Baselines3 reinforcement learning agents. It provides a structured codebase for training, tuning and using RL agents and ships with pre-trained agents that can be reused or evaluated. The repository focuses on enabling users of Stable Baselines3 to run experiments and apply hyperparameter optimization to improve agent performance. The intended audience includes researchers and practitioners who want ready-to-use agent implementations, repeatable training workflows and examples of trained policies. The project centralizes common training tasks for reinforcement learning into one repository to lower the barrier to entry for applying and comparing SB3 algorithms.
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App Details
Features
Integration with Stable Baselines3 is the core feature, enabling use of established RL algorithms with the repository"s training framework. The project explicitly offers hyperparameter optimization capabilities to tune agent performance. It includes pre-trained agents so users can inspect, evaluate, or reuse trained policies without retraining from scratch. The framework aggregates training-related materials and configurations around SB3 agents, serving as a reference collection of agent setups and trained models. The repository organization and provided artifacts aim to simplify experimentation with reinforcement learning using SB3.
Use Cases
The repository helps practitioners and researchers accelerate reinforcement learning experiments by providing a curated training framework for Stable Baselines3. Pre-trained agents let users quickly evaluate baselines, test deployment scenarios, or use existing models for transfer without the initial computational cost of training. Built-in hyperparameter optimization support helps improve agent performance and reduces manual tuning effort. By centralizing SB3-compatible training recipes and artifacts, the project saves setup time, supports comparison across algorithms and configurations, and provides a practical starting point for development, evaluation and experimentation in RL projects.
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