Rulebook: bringing co-routines to reinforcement learning environments

2025-04-28

AI Summary

Reinforcement learning (RL) environments are commonly implemented either as isolated processes—incuring inter-process synchronization overhead—or as hand-crafted state machines—imposing unstructured programming burdens—severely limiting scalability and development efficiency. This paper introduces Rulebook, a coroutine-based domain-specific language (DSL) for RL environment specification, compiled to efficient low-level code. Its core innovation is the first systematic decoupling of coroutine semantics from syntactic expression: the compiler automatically lowers high-level coroutine programs into zero-overhead, deterministic finite-state machines with no runtime communication or manual state management. This enables modular, composable, and maintainable simulation logic while preserving full determinism and performance. Empirical evaluation demonstrates that Rulebook reduces development effort for large-scale RL environments by over 50%, significantly improving scalability, correctness assurance, and engineering practicality.

Technology Category

• Machine Learning: Reinforcement Learning
• Multiagent Systems: Multiagent Learning
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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.

Problem

• Simplifying RL environment interfaces to reduce constraints
• Eliminating synchronization and communication overheads in RL environments
• Decoupling execution model from syntax for easier environment creation

Innovation

• Co-routine-based compiled language for RL environments
• Automatically generates state machines for ML interaction
• Decouples execution model from syntactical encoding