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Behaviour Suite for Reinforcement Learning

arXiv 1908.03568 ↗First posted 9 Aug 2019cs.LG cs.AI stat.MLcited by 129

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Abstract (arXiv)

This paper introduces the Behaviour Suite for Reinforcement Learning, or bsuite for short. bsuite is a collection of carefully-designed experiments that investigate core capabilities of reinforcement learning (RL) agents with two objectives. First, to collect clear, informative and scalable problems that capture key issues in the design of general and efficient learning algorithms. Second, to study agent behaviour through their performance on these shared benchmarks. To complement this effort, we open source github.com/deepmind/bsuite, which automates evaluation and analysis of any agent on bsuite. This library facilitates reproducible and accessible research on the core issues in RL, and ultimately the design of superior learning algorithms. Our code is Python, and easy to use within existing projects. We include examples with OpenAI Baselines, Dopamine as well as new reference implementations. Going forward, we hope to incorporate more excellent experiments from the research community, and commit to a periodic review of bsuite from a committee of prominent researchers.

Key facts. Cited by 129 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Aug 2019.

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Citing papers per year: 2020: 1; 2021: 29; 2022: 15; 2023: 35; 2024: 20; 2025: 16; 2026: 13.

129 papers.

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  1. 2023Mastering Diverse Domains through World Modelscited by 1,193
  2. 2021A Minimalist Approach to Offline Reinforcement Learningcited by 882
  3. 2021Benchmarking the Spectrum of Agent Capabilitiescited by 193
  4. 2021A Survey of Zero-shot Generalisation in Deep Reinforcement Learningcited by 164
  5. 2022Exploration in Deep Reinforcement Learning: A Surveycited by 158
  6. 2021Epistemic Neural Networkscited by 120
  7. 2023Structured State Space Models for In-Context Reinforcement Learningcited by 109
  8. 2021DEUP: Direct Epistemic Uncertainty Predictioncited by 102
  9. 2021Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Potcited by 97
  10. 2021MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Researchcited by 96
  11. 2021Reward is enough for convex MDPscited by 82
  12. 2024Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learningcited by 80
38 more
  1. 2024Simplifying Deep Temporal Difference Learningcited by 71
  2. 2022Automated Reinforcement Learning (AutoRL): A Survey and Open Problemscited by 69
  3. 2023When Do Transformers Shine in RL? Decoupling Memory from Credit Assignmentcited by 62
  4. 2021Reinforcement Learning, Bit by Bitcited by 59
  5. 2023POPGym: Benchmarking Partially Observable Reinforcement Learningcited by 59
  6. 2023JaxMARL: Multi-Agent RL Environments and Algorithms in JAXcited by 59
  7. 2021Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learningcited by 53
  8. 2023Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAXcited by 52
  9. 2023Pgx: Hardware-Accelerated Parallel Game Simulators for Reinforcement Learningcited by 45
  10. 2024Mastering Memory Tasks with World Modelscited by 38
  11. 2021Reinforcement Learning for Adaptive Mesh Refinementcited by 37
  12. 2021On Bonus-Based Exploration Methods in the Arcade Learning Environmentcited by 37
  13. 2021CORA: Benchmarks, Baselines, and Metrics as a Platform for Continual Reinforcement Learning Agentscited by 36
  14. 2021Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agentscited by 35
  15. 2024Gymnasium: A Standard Interface for Reinforcement Learning Environmentscited by 35
  16. 2025Memory, Benchmark & Robots: A Benchmark for Solving Complex Tasks with Reinforcement Learningcited by 34
  17. 2021Geometric Entropic Explorationcited by 32
  18. 2021Regularized Behavior Value Estimationcited by 32
  19. 2023Approximate Thompson Sampling via Epistemic Neural Networkscited by 31
  20. 2023Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlocited by 29
  21. 2022The Phenomenon of Policy Churncited by 28
  22. 2022Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learningcited by 27
  23. 2022Sample Efficient Deep Reinforcement Learning via Uncertainty Estimationcited by 26
  24. 2021Decoupled Reinforcement Learning to Stabilise Intrinsically-Motivated Explorationcited by 25
  25. 2022An Empirical Study of Implicit Regularization in Deep Offline RLcited by 24
  26. 2021On Instrumental Variable Regression for Deep Offline Policy Evaluationcited by 22
  27. 2021CARL: A Benchmark for Contextual and Adaptive Reinforcement Learningcited by 21
  28. 2023LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarioscited by 20
  29. 2023ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPscited by 19
  30. 2021TeachMyAgent: a Benchmark for Automatic Curriculum Learning in Deep RLcited by 18
  31. 2021A general class of surrogate functions for stable and efficient reinforcement learningcited by 16
  32. 2022Reinforcement learning for automatic quadrilateral mesh generation: a soft actor-critic approachcited by 16
  33. 2024NAVIX: Scaling MiniGrid Environments with JAXcited by 16
  34. 2024Can Learned Optimization Make Reinforcement Learning Less Difficult?cited by 14
  35. 2022Learning to Generalize with Object-centric Agents in the Open World Survival Game Craftercited by 13
  36. 2023GUARD: A Safe Reinforcement Learning Benchmarkcited by 13
  37. 2023Inferring Capabilities from Task Performance with Bayesian Triangulationcited by 13
  38. 2023Learning to Optimize for Reinforcement Learningcited by 12

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