CoRL: Environment Creation and Management Focused on System Integration

• URL: http://arxiv.org/abs/2303.02182v1
• Date: Fri, 3 Mar 2023 19:01:53 GMT
• Title: CoRL: Environment Creation and Management Focused on System Integration
• Authors: Justin D. Merrick, Benjamin K. Heiner, Cameron Long, Brian Stieber, Steve Fierro, Vardaan Gangal, Madison Blake, Joshua Blackburn
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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