CaiRL: A High-Performance Reinforcement Learning Environment Toolkit

@article{Andersen2022CaiRLAH,
  title={CaiRL: A High-Performance Reinforcement Learning Environment Toolkit},
  author={Per-Arne Andersen and Morten Goodwin and Ole-Christoffer Granmo},
  journal={2022 IEEE Conference on Games (CoG)},
  year={2022},
  pages={361-368}
}
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… 
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