Rlpy: A Value-Function-Based Reinforcement Learning Framework for Education and Research

• Main Authors: Dann, Christoph (Author), Dabney, William (Author), Geramifard, Alborz (Contributor), Klein, Robert Henry (Contributor), How, Jonathan P. (Contributor)
• Other Authors: Massachusetts Institute of Technology. Laboratory for Information and Decision Systems (Contributor)
• Format: Article
• Language: English
• Published: MIT Press, 2016-12-07T19:45:44Z.
• Subjects: Article
• Online Access: Get fulltext

 RLPy is an object-oriented reinforcement learning software package with a focus on valuefunction-based methods using linear function approximation and discrete actions. The framework was designed for both educational and research purposes. It provides a rich library of fine-grained, easily exchangeable components for learning agents (e.g., policies or representations of value functions), facilitating recently increased specialization in reinforcement learning. RLPy is written in Python to allow fast prototyping, but is also suitable for large-scale experiments through its built-in support for optimized numerical libraries and parallelization. Code profiling, domain visualizations, and data analysis are integrated in a self-contained package available under the Modified BSD License at http://github.com/rlpy/rlpy. All of these properties allow users to compare various reinforcement learning algorithms with little effort.