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

Abstract

Model-free Reinforcement Learning (RL) requires the ability to sample trajectories by taking actions in the original problem environment or a simulated version of it. Breakthroughs in the field of RL have been largely facilitated by the development of dedicated open source simulators with easy to use frameworks such as OpenAI Gym and its Atari environments. In this paper we propose to use the OpenAI Gym framework on discrete event time based Discrete Event Multi-Agent Simulation (DEMAS). We introduce a general technique to wrap a DEMAS simulator into the Gym framework. We expose the technique in detail and implement it using the simulator ABIDES as a base. We apply this work by specifically using the markets extension of ABIDES, ABIDES-Markets, and develop two benchmark financial markets OpenAI Gym environments for training daily investor and execution agents. 1 As a result, these two environments describe classic financial problems with a complex interactive market behavior response to the experimental agent's action.

CTMSTOU driven markets: simulated environment for regime-awareness in trading policies

“…An important number of simulation platform have been developed such as (Wang et al 2017) or (Wang et al 2021) and have reported promising results. As highlighted in (Amrouni et al 2021), ABIDES (Byrd, Hybinette, and Balch 2020) is a very flexible discrete time multiagent event simulator with an already advanced extension to financial markets ABIDES-Markets. We choose to build upon ABIDES-Markets in this work.…”