mlsanityData to 14 Oct 2026

Rulebook: bringing co-routines to reinforcement learning environments

arXiv 2504.19625 ↗First posted 28 Apr 2025cs.PL cs.LGcited by 0

On the map: Deep reinforcement learning methods → Deep RL algorithms and representations

Abstract (arXiv)

Reinforcement learning (RL) algorithms, due to their reliance on external systems to learn from, require digital environments (e.g., simulators) with very simple interfaces, which in turn constrain significantly the implementation of such environments. In particular, these environments are implemented either as separate processes or as state machines, leading to synchronization and communication overheads in the first case, and to unstructured programming in the second. We propose a new domain-specific, co-routine-based, compiled language, called Rulebook, designed to automatically generate the state machine required to interact with machine learning (ML) algorithms and similar applications, with no performance overhead. Rulebook allows users to express programs without needing to be aware of the specific interface required by the ML components. By decoupling the execution model of the program from the syntactical encoding of the program, and thus without the need for manual state management, Rulebook allows to create larger and more sophisticated environments at a lower development cost.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods. Builds on 7 of its 17 references that are in the map. First posted on arXiv 28 Apr 2025.

Builds on

7 of 17 references.

References · 7

  1. 2019Mastering Atari, Go, Chess and Shogi by Planning with a Learned Modelcited by 1,353
  2. 2021Mastering Atari Games with Limited Datacited by 237
  3. 2019OpenSpiel: A Framework for Reinforcement Learning in Gamescited by 186
  4. 2020Explainable Reinforcement Learning: A Surveycited by 141
  5. 2020MLIR: A Compiler Infrastructure for the End of Moore's Lawcited by 65
  6. 2023Pgx: Hardware-Accelerated Parallel Game Simulators for Reinforcement Learningcited by 51
  7. 2020Compiling ONNX Neural Network Models Using MLIRcited by 18

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  1. 2025PyTupli: A Scalable Infrastructure for Collaborative Offline Reinforcement Learning Projects
  2. 2026RLLBC-Lib: An Educational Code Library for Reinforcement Learning and Learning-Based Control
  3. 2024Towards a Domain-Specific Modelling Environment for Reinforcement Learning
  4. 2024Programmatic Reinforcement Learning: Navigating Gridworlds
  5. 2024TeaMs-RL: Teaching LLMs to Generate Better Instruction Datasets via Reinforcement Learning
  6. 2025Rule-Guided Reinforcement Learning Policy Evaluation and Improvement
  7. 2026MCP-Universe RL: A Framework for Training MCP Tool-Use Agents via Reinforcement Learning
  8. 2025Reinforcement Learning for Machine Learning Engineering Agents