ABIDES-MARL: A Multi-Agent Reinforcement Learning Environment for Optimal Execution with Endogenous Liquidity

By Patrick Cheridito, Jean-Loup Dupret, Zhexin Wu

Published 2026-08-24

Everscope rating

1618.9

Relevance to quantitative trading

9 / 10

Implementation complexity

8 / 10

Reproducibility

4 / 5

About this paper

Methodology: ABIDES-MARL: Synchronized Multi-Agent Reinforcement Learning in Limit Order Book Simulation. Problem types: Algorithmic Execution, Market Making, Reinforcement Learning, Optimization, Multi-agent Stochastic Game, Equilibrium Approximation.

arXiv:2511.02016 · Code · Paper rankings

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