mlsanityData to 7 Oct 2026

Gymnasium: A Standard Interface for Reinforcement Learning Environments

arXiv 2407.17032 ↗First posted 24 Jul 2024cs.LG cs.DLcited by 35

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Abstract (arXiv)

Reinforcement Learning (RL) is a continuously growing field that has the potential to revolutionize many areas of artificial intelligence. However, despite its promise, RL research is often hindered by the lack of standardization in environment and algorithm implementations. This makes it difficult for researchers to compare and build upon each other's work, slowing down progress in the field. Gymnasium is an open-source library that provides a standard API for RL environments, aiming to tackle this issue. Gymnasium's main feature is a set of abstractions that allow for wide interoperability between environments and training algorithms, making it easier for researchers to develop and test RL algorithms. In addition, Gymnasium provides a collection of easy-to-use environments, tools for easily customizing environments, and tools to ensure the reproducibility and robustness of RL research. Through this unified framework, Gymnasium significantly streamlines the process of developing and testing RL algorithms, enabling researchers to focus more on innovation and less on implementation details. By providing a standardized platform for RL research, Gymnasium helps to drive forward the field of reinforcement learning and unlock its full potential. Gymnasium is available online at https://github.com/Farama-Foundation/Gymnasium

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). Builds on 25 of its 48 references that are in the map. First posted on arXiv 24 Jul 2024.

Builds on

25 of 48 references.

References · 25

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  3. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  4. 2019Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learningcited by 1,168
  5. 2017Hindsight Experience Replaycited by 1,096
  6. 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,084
  7. 2021Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulationcited by 375
  8. 2020dm_control: Software and Tasks for Continuous Controlcited by 368
  9. 2023Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Taskscited by 320
  10. 2018Reinforcement Learning on Web Interfaces Using Workflow-Guided Explorationcited by 282
  11. 2020PettingZoo: Gym for Multi-Agent Reinforcement Learningcited by 209
  12. 2016ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learningcited by 178
13 more
  1. 2020Acme: A Research Framework for Distributed Reinforcement Learningcited by 146
  2. 2021Tianshou: a Highly Modularized Deep Reinforcement Learning Librarycited by 139
  3. 2019Behaviour Suite for Reinforcement Learningcited by 129
  4. 2018Dopamine: A Research Framework for Deep Reinforcement Learningcited by 121
  5. 2022EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Enginecited by 58
  6. 2023Pgx: Hardware-Accelerated Parallel Game Simulators for Reinforcement Learningcited by 45
  7. 2023OCAtari: Object-Centric Atari 2600 Reinforcement Learning Environmentscited by 30
  8. 2024PufferLib: Making Reinforcement Learning Libraries and Environments Play Nicecited by 22
  9. 2024Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learningcited by 17
  10. 2020Multiplayer Support for the Arcade Learning Environmentcited by 8
  11. 2023PyFlyt -- UAV Simulation Environments for Reinforcement Learning Researchcited by 7
  12. 2024ICU-Sepsis: A Benchmark MDP Built from Real Medical Datacited by 6
  13. 2022Mask Atari for Deep Reinforcement Learning as POMDP Benchmarkscited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 16; 2026: 18.

35 papers.

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  1. 2025A Survey of Reinforcement Learning for Large Reasoning Modelscited by 175
  2. 2025VAGEN: Reinforcing World Model Reasoning for Multi-Turn VLM Agentscited by 60
  3. 2026Gym-Anything: Turn any Software into an Agent Environmentcited by 24
  4. 2025From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligencecited by 13
  5. 2025Reward Learning from Multiple Feedback Typescited by 9
  6. 2025General Modular Harness for LLM Agents in Multi-Turn Gaming Environmentscited by 9
  7. 2026VisGym: Diverse, Customizable, Scalable Environments for Multimodal Agentscited by 9
  8. 2025KORGym: A Dynamic Game Platform for LLM Reasoning Evaluationcited by 7
  9. 2026GameVerse: Can Vision-Language Models Learn from Video-based Reflection?cited by 7
  10. 2025Learning Without Critics? Revisiting GRPO in Classical Reinforcement Learning Environmentscited by 5
  11. 2025Agent-Environment Alignment via Automated Interface Generationcited by 4
  12. 2026AdaReasoner: Dynamic Tool Orchestration for Iterative Visual Reasoningcited by 4
23 more
  1. 2025Free Energy Risk Metrics for Systemically Safe AI: Gatekeeping Multi-Agent Studycited by 2
  2. 2025FM-IRL: Flow-Matching for Reward Modeling and Policy Regularization in Reinforcement Learningcited by 2
  3. 2025Prompted Policy Search: Reinforcement Learning through Linguistic and Numerical Reasoning in LLMscited by 2
  4. 2026Hierarchical Experimentalist Agentscited by 2
  5. 2026GraphIR: Architecture-Level Search States for LLM-Guided Neural Architecture Evolutioncited by 2
  6. 2025Rule-Bottleneck Reinforcement Learning: Joint Explanation and Decision Optimization for Resource Allocation with Language Agentscited by 1
  7. 2025Characterizing the Robustness of Black-Box LLM Planners Under Perturbed Observations with Adaptive Stress Testingcited by 1
  8. 2025Fine-tuning Behavioral Cloning Policies with Preference-Based Reinforcement Learningcited by 1
  9. 2026What You Think is What You See: Driving Exploration in VLM Agents via Visual-Linguistic Curiositycited by 1
  10. 2026When Memory Lies: An Empirical Study of Spatial Memory Staleness in VLM Agentscited by 1
  11. 2024Putting the Iterative Training of Decision Trees to the Test on a Real-World Robotic Taskcited by 0
  12. 2025HRM-Agent: Training a recurrent reasoning model in dynamic environments using reinforcement learningcited by 0
  13. 2025Operator Models for Continuous-Time Offline Reinforcement Learningcited by 0
  14. 2026Online World Modeling Enables Real-World Inverse Reinforcement Learning from Observationcited by 0
  15. 2026Learning When to Stop: Selective Imitation Learning Under Arbitrary Dynamics Shiftcited by 0
  16. 2026Thinking with Patterns: Breaking the Perceptual Bottleneck in Visual Planning via Pattern Inductioncited by 0
  17. 2026The Hitchhiker's Guide to Agentic AI: From Foundations to Systemscited by 0
  18. 2026On Locality and Length Generalization in Visual Reasoningcited by 0
  19. 2026Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learningcited by 0
  20. 2026Belayer: Efficient Fault Tolerance for LLM Agentic RL Trainingcited by 0
  21. 2026World Models Under Asynchronous Sensor Observationscited by 0
  22. 2026Temporally Interpretable Differentiable Decision Treescited by 0
  23. 2026A Geometric Approach to Soft Actor-Critic with Zonotopes for Locomotion Learningcited by 0