RLPy: A Value-Function-Based Reinforcement Learning Framework for Education and Re- search

RLPy (http: //acl. mit. edu/rlpy ) is an open-source reinforcement learning (RL) package with the focus on linear function approximation for value-based techniques and planning problems with discrete actions. The aim of this package is to: a) boost the RL education process, and b) enable crisp and easy to debug experimentation with existing and new methods. RLPy achieves these goals by providing a rich library of fine-grained, easily exchangeable components for learning agents (e. g. , policies or representations of value functions). Developed in Python, RLPy allows fast prototyping, yet harnesses the power of state-of- the-art numerical libraries such as scipy and parallelization to scale to large problems. Furthermore, RLPy is self-contained. The package includes code profiling, domain visualizations, and data analysis. Finally RLPy is available under the Modified BSD License that allows integration with 3rd party softwares with little legal entanglement.

Authors

• Alborz Geramifard Michael Bowling
• Christoph Dann
• Robert Klein
• William Dabney University of Massachusetts Amherst
• Jonathan How

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