mlsanityData to 14 Oct 2026

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

arXiv 2110.14771 ↗First posted 27 Oct 2021cs.MA cs.AI q-fin.TRcited by 36

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Abstract (arXiv)

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. As a result, these two environments describe classic financial problems with a complex interactive market behavior response to the experimental agent's action.

Key facts. Cited by 36 papers in the map (data through 14 Oct 2026). Builds on 9 of its 24 references that are in the map. First posted on arXiv 27 Oct 2021.

Builds on

9 of 24 references.

References · 9

  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  2. 2016OpenAI Gymcited by 1,736
  3. 2017Ray: A Distributed Framework for Emerging AI Applicationscited by 527
  4. 2018Tune: A Research Platform for Distributed Model Selection and Trainingcited by 395
  5. 2019Challenges of Real-World Reinforcement Learningcited by 291
  6. 2019OpenSpiel: A Framework for Reinforcement Learning in Gamescited by 186
  7. 2019Get Real: Realism Metrics for Robust Limit Order Book Market Simulationscited by 41
  8. 2019ABIDES: Towards High-Fidelity Market Simulation for AI Researchcited by 32
  9. 2019How to Evaluate Trading Strategies: Single Agent Market Replay or Multiple Agent Interactive Simulation?cited by 15

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 6; 2023: 8; 2024: 5; 2025: 6; 2026: 11.

36 papers.

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  1. 2023Alpha-GPT: Human-AI Interactive Alpha Mining for Quantitative Investmentcited by 56
  2. 2024MarS: a Financial Market Simulation Engine Powered by Generative Foundation Modelcited by 28
  3. 2024When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environmentscited by 26
  4. 2023Dynamic Datasets and Market Environments for Financial Reinforcement Learningcited by 15
  5. 2023Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilitiescited by 15
  6. 2023JAX-LOB: A GPU-Accelerated limit order book simulator to unlock large scale reinforcement learning for tradingcited by 14
  7. 2022Quant 4.0: Engineering Quantitative Investment with Automated, Explainable and Knowledge-driven Artificial Intelligencecited by 6
  8. 2023K-SHAP: Policy Clustering Algorithm for Anonymous Multi-Agent State-Action Pairscited by 6
  9. 2022Phantom -- A RL-driven multi-agent framework to model complex systemscited by 5
  10. 2024Optimal Execution with Reinforcement Learningcited by 5
  11. 2023Deep Calibration of Market Simulations using Neural Density Estimators and Embedding Networkscited by 4
  12. 2022Model-based gym environments for limit order book tradingcited by 3
24 more
  1. 2023Asynchronous Deep Double Duelling Q-Learning for Trading-Signal Execution in Limit Order Book Marketscited by 3
  2. 2024CNN-DRL with Shuffled Features in Financecited by 3
  3. 2022Limited or Biased: Modeling Sub-Rational Human Investors in Financial Marketscited by 2
  4. 2026FinEvo: From Isolated Backtests to Ecological Market Games for Multi-Agent Financial Strategy Evolutioncited by 2
  5. 2026OpenFinGym: A Verifiable Multi-Task Gym Environment for Evaluating Quant Agentscited by 2
  6. 2022Equitable Marketplace Mechanism Designcited by 1
  7. 2023INTAGS: Interactive Agent-Guided Simulationcited by 1
  8. 2024Empirical Equilibria in Agent-based Economic systems with Learning agentscited by 1
  9. 2025Finding Optimal Trading History in Reinforcement Learning for Stock Market Tradingcited by 1
  10. 2025DiffVolume: Diffusion Models for Volume Generation in Limit Order Bookscited by 1
  11. 2025Heterogeneous RBCs via Deep Multi-Agent Reinforcement Learningcited by 1
  12. 2022CTMSTOU driven markets: simulated environment for regime-awareness in trading policiescited by 0
  13. 2025Revisiting Ensemble Methods for Stock Trading and Crypto Trading Tasks at ACM ICAIF FinRL Contest 2023-2024cited by 0
  14. 2025Tail-Safe Hedging: Explainable Risk-Sensitive Reinforcement Learning with a White-Box CBF--QP Safety Layer in Arbitrage-Free Marketscited by 0
  15. 2025Natural-gas storage modelling by deep reinforcement learningcited by 0
  16. 2026Diverse Approaches to Optimal Execution Schedule Generationcited by 0
  17. 2026Decoupling Identity from Utility: Privacy-by-Design Frameworks for Financial Ecosystemscited by 0
  18. 2026Algometrics: Forecasting Under Algorithmic Feedbackcited by 0
  19. 2026MetaPS: Adaptive Programmatic Strategy Selection for Market Agentscited by 0
  20. 2026From Economic Agents to Agentic Economies: A Systems Blueprint for Economic World Modelscited by 0
  21. 2026CARD: Controlled Agentic Reddit Discussions for Credit Card Simulationcited by 0
  22. 2026SAiFE-gym: Model-based Environments for Automated Market Making with Concentrated Liquiditycited by 0
  23. 2026Multi-Agent Debate for Explainable Trading: Reasoning, Consensus, and Performance in Simulated Marketscited by 0
  24. 2026When and How Order-Flow Interventions Work After Dealers Adaptcited by 0