(user_guide)= # User Guide ## Introduction Welcome to rlberry. Use rlberry's [ExperimentManager](experimentManager_page) to train, evaluate and compare rl agents. Like other popular rl libraries, rlberry also provides basic tools for plotting, multiprocessing and logging . In this user guide, we take you through the core features of rlberry and illustrate them with [examples](/auto_examples/index) and [API documentation](/api) . To run all the examples, you will need to install other libraries like "[rlberry-scool](https://github.com/rlberry-py/rlberry-scool)" (and others). The easiest way to do it is : ```none pip install rlberry[torch,extras] pip install rlberry-scool ``` [rlberry-scool](https://github.com/rlberry-py/rlberry-scool) : It's the repository used for teaching purposes. These are mainly basic agents and environments, in a version that makes it easier for students to learn. You can find more details about installation [here](installation)! You can find our quick starts here : ```{toctree} :maxdepth: 2 basics/quick_start_rl/quickstart.md basics/DeepRLTutorial/TutorialDeepRL.md ``` ## Set up an experiment ```{include} templates/nice_toc.md ``` ```{toctree} :maxdepth: 2 basics/userguide/environment.md basics/userguide/agent.md basics/userguide/experimentManager.md basics/userguide/logging.md basics/userguide/visualization.md ``` ## Experimenting with Deep agents [(In construction)](https://github.com/rlberry-py/rlberry/issues/459) ## Reproducibility ```{toctree} :maxdepth: 2 basics/userguide/seeding.md basics/userguide/save_load.md basics/userguide/export_training_data.md ``` ## Advanced Usage ```{toctree} :maxdepth: 2 basics/userguide/adastop.md basics/comparison.md basics/userguide/external_lib.md ``` - Custom Agents (In construction) - Custom Environments (In construction) - Transfer Learning (In construction) # Contributing to rlberry If you want to contribute to rlberry, check out [the contribution guidelines](contributing).