mlsanityData to 14 Oct 2026

The NetHack Learning Environment

arXiv 2006.13760 ↗First posted 24 Jun 2020cs.LG cs.AI cs.CLcited by 198

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Cited by 35 in Deep reinforcement learning methods, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Progress in Reinforcement Learning (RL) algorithms goes hand-in-hand with the development of challenging environments that test the limits of current methods. While existing RL environments are either sufficiently complex or based on fast simulation, they are rarely both. Here, we present the NetHack Learning Environment (NLE), a scalable, procedurally generated, stochastic, rich, and challenging environment for RL research based on the popular single-player terminal-based roguelike game, NetHack. We argue that NetHack is sufficiently complex to drive long-term research on problems such as exploration, planning, skill acquisition, and language-conditioned RL, while dramatically reducing the computational resources required to gather a large amount of experience. We compare NLE and its task suite to existing alternatives, and discuss why it is an ideal medium for testing the robustness and systematic generalization of RL agents. We demonstrate empirical success for early stages of the game using a distributed Deep RL baseline and Random Network Distillation exploration, alongside qualitative analysis of various agents trained in the environment. NLE is open source at https://github.com/facebookresearch/nle.

Key facts. Cited by 198 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Jun 2020.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2020: 1; 2021: 17; 2022: 32; 2023: 34; 2024: 38; 2025: 41; 2026: 35.

198 papers.

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  1. 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 2,006
  2. 2023AgentBench: Evaluating LLMs as Agentscited by 1,267
  3. 2022MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledgecited by 478
  4. 2021Benchmarking the Spectrum of Agent Capabilitiescited by 200
  5. 2022Evolving Curricula with Regret-Based Environment Designcited by 173
  6. 2021A Survey of Zero-shot Generalisation in Deep Reinforcement Learningcited by 170
  7. 2024Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learningcited by 158
  8. 2024BALROG: Benchmarking Agentic LLM and VLM Reasoning On Gamescited by 146
  9. 2023Human-Timescale Adaptation in an Open-Ended Task Spacecited by 138
  10. 2025REASONING GYM: Reasoning Environments for Reinforcement Learning with Verifiable Rewardscited by 130
  11. 2023SmartPlay: A Benchmark for LLMs as Intelligent Agentscited by 125
  12. 2022SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learningcited by 122
38 more
  1. 2021Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observabilitycited by 109
  2. 2023Motif: Intrinsic Motivation from Artificial Intelligence Feedbackcited by 102
  3. 2024AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM Agentscited by 102
  4. 2024DISCOVERYWORLD: A Virtual Environment for Developing and Evaluating Automated Scientific Discovery Agentscited by 100
  5. 2021MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Researchcited by 97
  6. 2024Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learningcited by 88
  7. 2021Decoupling Value and Policy for Generalization in Reinforcement Learningcited by 82
  8. 2024Simplifying Deep Temporal Difference Learningcited by 78
  9. 2024SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learningcited by 76
  10. 2022BYOL-Explore: Exploration by Bootstrapped Predictioncited by 73
  11. 2022Automated Reinforcement Learning (AutoRL): A Survey and Open Problemscited by 70
  12. 2022A Generalist Neural Algorithmic Learnercited by 59
  13. 2022Exploration via Elliptical Episodic Bonusescited by 51
  14. 2022Improving Intrinsic Exploration with Language Abstractionscited by 49
  15. 2022Contextualize Me -- The Case for Context in Reinforcement Learningcited by 46
  16. 2021Grounding Language to Entities and Dynamics for Generalization in Reinforcement Learningcited by 45
  17. 2023AMAGO: Scalable In-Context Reinforcement Learning for Adaptive Agentscited by 41
  18. 2023Generalization to New Sequential Decision Making Tasks with In-Context Learningcited by 39
  19. 2021CORA: Benchmarks, Baselines, and Metrics as a Platform for Continual Reinforcement Learning Agentscited by 38
  20. 2021Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agentscited by 36
  21. 2025Memory, Benchmark & Robots: A Benchmark for Solving Complex Tasks with Reinforcement Learningcited by 36
  22. 2025VideoGameBench: Can Vision-Language Models complete popular video games?cited by 36
  23. 2024V-IRL: Grounding Virtual Intelligence in Real Lifecited by 35
  24. 2023LLF-Bench: Benchmark for Interactive Learning from Language Feedbackcited by 33
  25. 2024Intelligent Go-Explore: Standing on the Shoulders of Giant Foundation Modelscited by 33
  26. 2024LMAct: A Benchmark for In-Context Imitation Learning with Long Multimodal Demonstrationscited by 32
  27. 2021RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learningcited by 31
  28. 2023On the Importance of Exploration for Generalization in Reinforcement Learningcited by 31
  29. 2021Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environmentscited by 28
  30. 2023Emergence of In-Context Reinforcement Learning from Noise Distillationcited by 27
  31. 2022MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learnedcited by 26
  32. 2023The Generalization Gap in Offline Reinforcement Learningcited by 26
  33. 2024Skill Set Optimization: Reinforcing Language Model Behavior via Transferable Skillscited by 26
  34. 2024PufferLib: Making Reinforcement Learning Libraries and Environments Play Nicecited by 24
  35. 2024MaestroMotif: Skill Design from Artificial Intelligence Feedbackcited by 24
  36. 2024Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation Problemcited by 23
  37. 2021CARL: A Benchmark for Contextual and Adaptive Reinforcement Learningcited by 21
  38. 2024Retrieval-Augmented Decision Transformer: External Memory for In-context RLcited by 21

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