A collection of tools for doing reinforcement learning research in Julia. Provide elaborately designed components and interfaces to help users implement new algorithms. Make it easy for new users to run benchmark experiments, compare different algorithms, and evaluate and diagnose agents. Facilitate reproducibility from traditional tabular methods to modern deep reinforcement learning algorithms. Make it easy for new users to run benchmark experiments, compare different algorithms, and evaluate and diagnose agents. Facilitate reproducibility from traditional tabular methods to modern deep reinforcement learning algorithms. Provide elaborately designed components and interfaces to help users implement new algorithms. A number of built-in environments and third-party environment wrappers are provided to evaluate algorithms in various scenarios.
Features
- Easy experimentation
- Reproducibility
- Reusability and extensibility
- Feature-rich Environments
- ReinforcementLearning.jl is a wrapper package which contains a collection of different packages
- You can simply run many built-in experiments in 3 lines
Project Samples
