mlsanityData to 7 Oct 2026

AndroidEnv: A Reinforcement Learning Platform for Android

arXiv 2105.13231 ↗First posted 27 May 2021cs.LG cs.AIcited by 90

Not on the map. Why?

Cited by 54 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We introduce AndroidEnv, an open-source platform for Reinforcement Learning (RL) research built on top of the Android ecosystem. AndroidEnv allows RL agents to interact with a wide variety of apps and services commonly used by humans through a universal touchscreen interface. Since agents train on a realistic simulation of an Android device, they have the potential to be deployed on real devices. In this report, we give an overview of the environment, highlighting the significant features it provides for research, and we present an empirical evaluation of some popular reinforcement learning agents on a set of tasks built on this platform.

Key facts. Cited by 90 papers in the map (data through 7 Oct 2026). Builds on 15 of its 38 references that are in the map. First posted on arXiv 27 May 2021.

Builds on

15 of 38 references.

References · 15

  1. 2015Continuous control with deep reinforcement learningcited by 3,279
  2. 2018IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architecturescited by 866
  3. 2018Maximum a Posteriori Policy Optimisationcited by 318
  4. 2018Reinforcement Learning on Web Interfaces Using Workflow-Guided Explorationcited by 282
  5. 2017Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agentscited by 267
  6. 2019MineRL: A Large-Scale Dataset of Minecraft Demonstrationscited by 210
  7. 2018Distributed Distributional Deterministic Policy Gradientscited by 195
  8. 2017Learning to Plan Chemical Synthesescited by 175
  9. 2020Acme: A Research Framework for Distributed Reinforcement Learningcited by 146
  10. 2016A Deep Hierarchical Approach to Lifelong Learning in Minecraftcited by 90
  11. 2017Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learningcited by 77
  12. 2016Control of Memory, Active Perception, and Action in Minecraftcited by 61
3 more
  1. 2018Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modelingcited by 31
  2. 2019Improving Deep Reinforcement Learning in Minecraft with Action Advicecited by 10
  3. 2016Exploratory Gradient Boosting for Reinforcement Learning in Complex Domainscited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 6; 2023: 14; 2024: 27; 2025: 23; 2026: 19.

90 papers.

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  1. 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 1,908
  2. 2023AgentBench: Evaluating LLMs as Agentscited by 1,211
  3. 2022WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agentscited by 1,197
  4. 2024OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environmentscited by 1,134
  5. 2022MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledgecited by 469
  6. 2024AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agentscited by 450
  7. 2022VIMA: General Robot Manipulation with Multimodal Promptscited by 403
  8. 2023Augmented Language Models: a Surveycited by 398
  9. 2024OS-ATLAS: A Foundation Action Model for Generalist GUI Agentscited by 370
  10. 2023A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesiscited by 350
  11. 2023Android in the Wild: A Large-Scale Dataset for Android Device Controlcited by 314
  12. 2024Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Securitycited by 257
38 more
  1. 2024DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learningcited by 210
  2. 2023Synapse: Trajectory-as-Exemplar Prompting with Memory for Computer Controlcited by 180
  3. 2023AutoDroid: LLM-powered Task Automation in Androidcited by 178
  4. 2025WebAgent-R1: Training Web Agents via End-to-End Multi-Turn Reinforcement Learningcited by 162
  5. 2024Large Language Model-Brained GUI Agents: A Surveycited by 160
  6. 2023Multimodal Web Navigation with Instruction-Finetuned Foundation Modelscited by 159
  7. 2024AssistantBench: Can Web Agents Solve Realistic and Time-Consuming Tasks?cited by 155
  8. 2024Autonomous Evaluation and Refinement of Digital Agentscited by 136
  9. 2024Aria-UI: Visual Grounding for GUI Instructionscited by 136
  10. 2025Scaling Computer-Use Grounding via User Interface Decomposition and Synthesiscited by 132
  11. 2024AndroidLab: Training and Systematic Benchmarking of Android Autonomous Agentscited by 105
  12. 2025Perception, Reason, Think, and Plan: A Survey on Large Multimodal Reasoning Modelscited by 89
  13. 2022A data-driven approach for learning to control computerscited by 87
  14. 2025UI-Vision: A Desktop-centric GUI Benchmark for Visual Perception and Interactioncited by 68
  15. 2023Vision-Language Models as a Source of Rewardscited by 65
  16. 2024MobileAgentBench: An Efficient and User-Friendly Benchmark for Mobile LLM Agentscited by 63
  17. 2024Understanding the Weakness of Large Language Model Agents within a Complex Android Environmentcited by 60
  18. 2023Explore, Select, Derive, and Recall: Augmenting LLM with Human-like Memory for Mobile Task Automationcited by 57
  19. 2024Mobile-Bench: An Evaluation Benchmark for LLM-based Mobile Agentscited by 56
  20. 2024MMInA: Benchmarking Multihop Multimodal Internet Agentscited by 55
  21. 2024Benchmarking Mobile Device Control Agents across Diverse Configurationscited by 54
  22. 2024Ferret-UI 2: Mastering Universal User Interface Understanding Across Platformscited by 51
  23. 2024MobileSafetyBench: Evaluating Safety of Autonomous Agents in Mobile Device Controlcited by 49
  24. 2023ASSISTGUI: Task-Oriented Desktop Graphical User Interface Automationcited by 45
  25. 2024AgentStudio: A Toolkit for Building General Virtual Agentscited by 45
  26. 2025MobileRL: Online Agentic Reinforcement Learning for Mobile GUI Agentscited by 43
  27. 2024LlamaTouch: A Faithful and Scalable Testbed for Mobile UI Task Automationcited by 36
  28. 2023Mobile-Env: Building Qualified Evaluation Benchmarks for LLM-GUI Interactioncited by 35
  29. 2024VideoGUI: A Benchmark for GUI Automation from Instructional Videoscited by 35
  30. 2025A Comprehensive Survey of Agents for Computer Use: Foundations, Challenges, and Future Directionscited by 33
  31. 2024Foundations and Recent Trends in Multimodal Mobile Agents: A Surveycited by 31
  32. 2022UGIF: UI Grounded Instruction Followingcited by 25
  33. 2023WebVLN: Vision-and-Language Navigation on Websitescited by 19
  34. 2025Scaling Autonomous Agents via Automatic Reward Modeling And Planningcited by 19
  35. 2025VEM: Environment-Free Exploration for Training GUI Agent with Value Environment Modelcited by 19
  36. 2024Tur[k]ingBench: A Challenge Benchmark for Web Agentscited by 18
  37. 2025Mirage-1: Augmenting and Updating GUI Agent with Hierarchical Multimodal Skillscited by 18
  38. 2025Mobile-Bench-v2: A More Realistic and Comprehensive Benchmark for VLM-based Mobile Agentscited by 16

50 most cited of 90 All 90 (JSON)