  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.
