CaiRL: A High-Performance Reinforcement Learning Environment Toolkit

• URL: http://arxiv.org/abs/2210.01235v1
• Date: Mon, 3 Oct 2022 21:24:04 GMT
• Title: CaiRL: A High-Performance Reinforcement Learning Environment Toolkit
• Authors: Per-Arne Andersen and Morten Goodwin and Ole-Christoffer Granmo
• Abstract summary: CaiRL Environment Toolkit is an efficient, compatible, and more sustainable alternative for training learning agents. We demonstrate the effectiveness of CaiRL in the classic control benchmark, comparing the execution speed to OpenAI Gym.
• Score: 9.432068833600884
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper addresses the dire need for a platform that efficiently provides a framework for running reinforcement learning (RL) experiments. We propose the CaiRL Environment Toolkit as an efficient, compatible, and more sustainable alternative for training learning agents and propose methods to develop more efficient environment simulations. There is an increasing focus on developing sustainable artificial intelligence. However, little effort has been made to improve the efficiency of running environment simulations. The most popular development toolkit for reinforcement learning, OpenAI Gym, is built using Python, a powerful but slow programming language. We propose a toolkit written in C++ with the same flexibility level but works orders of magnitude faster to make up for Python's inefficiency. This would drastically cut climate emissions. CaiRL also presents the first reinforcement learning toolkit with a built-in JVM and Flash support for running legacy flash games for reinforcement learning research. We demonstrate the effectiveness of CaiRL in the classic control benchmark, comparing the execution speed to OpenAI Gym. Furthermore, we illustrate that CaiRL can act as a drop-in replacement for OpenAI Gym to leverage significantly faster training speeds because of the reduced environment computation time.

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