  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.
