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Model-free Reinforcement Learning (RL) requires the ability to sampletrajectories by taking actions in the original problem environment or asimulated version of it. Breakthroughs in the field of RL have been largelyfacilitated by the development of dedicated open source simulators with easy touse frameworks such as OpenAI Gym and its Atari environments. In this paper wepropose to use the OpenAI Gym framework on discrete event time based DiscreteEvent Multi-Agent Simulation (DEMAS). We introduce a general technique to wrapa DEMAS simulator into the Gym framework. We expose the technique in detail andimplement it using the simulator ABIDES as a base. We apply this work byspecifically using the markets extension of ABIDES, ABIDES-Markets, and developtwo benchmark financial markets OpenAI Gym environments for training dailyinvestor and execution agents. As a result, these two environments describeclassic financial problems with a complex interactive market behavior responseto the experimental agent's action.

ABIDES-Gym: Gym Environments for Multi-Agent Discrete Event Simulation and Application to Financial Markets