  Existing reinforcement learning environment libraries use monolithic
environment classes, provide shallow methods for altering agent observation and
action spaces, and/or are tied to a specific simulation environment. The Core
Reinforcement Learning library (CoRL) is a modular, composable, and
hyper-configurable environment creation tool. It allows minute control over
agent observations, rewards, and done conditions through the use of
easy-to-read configuration files, pydantic validators, and a functor design
pattern. Using integration pathways allows agents to be quickly implemented in
new simulation environments, encourages rapid exploration, and enables
transition of knowledge from low-fidelity to high-fidelity simulations.
Natively multi-agent design and integration with Ray/RLLib (Liang et al., 2018)
at release allow for easy scalability of agent complexity and computing power.
The code is publicly released and available at
https://github.com/act3-ace/CoRL.
