Discrete‐Event Simulation of Computer and Communication Systems

Hartmann, Alfred; Schwetman, Herb

doi:10.1002/9780470172445.ch20

Cited by 2 publications

(1 citation statement)

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“…In that case, the easiest way to model the transition from a state observed by an agent to the next state some time in the future after it took an action is to simulate the actions taken by all the agents in the system. Discrete Event Multi-Agent Simulation (DEMAS) has been a topic of interest for a long time [7,8]. There are two main types of DEMAS:…”

Section: Introductionmentioning

confidence: 99%

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

Amrouni,

Moulin,

Vann

et al. 2021

Preprint

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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.

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Section: Introductionmentioning

confidence: 99%