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arXiv · 2201.09568

Pearl: Parallel Evolutionary and Reinforcement Learning Library

Rohan Tangri·Danilo P. Mandic·Anthony G. Constantinides
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Abstract

Reinforcement learning is increasingly finding success across domains where the problem can be represented as a Markov decision process. Evolutionary computation algorithms have also proven successful in this domain, exhibiting similar performance to the generally more complex reinforcement learning. Whilst there exist many open-source reinforcement learning and evolutionary computation libraries, no publicly available library combines the two approaches for enhanced comparison, cooperation, or visualization. To this end, we have created Pearl (https://github.com/LondonNode/Pearl), an open source Python library designed to allow researchers to rapidly and conveniently perform optimized reinforcement learning, evolutionary computation and combinations of the two. The key features within Pearl include: modular and expandable components, opinionated module settings, Tensorboard integration, custom callbacks and comprehensive visualizations.

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Rohan Tangri, Danilo P. Mandic, Anthony G. Constantinides. 2022-01-24. Pearl: Parallel Evolutionary and Reinforcement Learning Library. https://arxiv.org/abs/2201.09568

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