Automatic Generation of High-Performance RL Environments

2026-03-12

AI Summary

This work proposes a reusable, automated framework for generating high-performance reinforcement learning (RL) environments at low cost (<$10), circumventing the months of specialized engineering typically required. By integrating generic prompt templates, large language model–driven code generation, high-performance Rust/JAX backends, hierarchical validation (encompassing properties, interactions, and trajectories), and agent-assisted repair, the method enables the creation of semantically equivalent RL environments from scratch. Evaluated across five benchmarks, the approach demonstrates substantial gains: TCGJax achieves 153K steps per second (6.6× faster than its Python counterpart), PokeJAX exhibits a 22,320× speedup, and environment overhead is reduced to under 4% of total training time, effectively closing the sim-to-sim policy transfer gap.

Technology Category

• Machine Learning: Reinforcement Learning
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Translating complex reinforcement learning (RL) environments into high-performance implementations has traditionally required months of specialized engineering. We present a reusable recipe - a generic prompt template, hierarchical verification, and iterative agent-assisted repair - that produces semantically equivalent high-performance environments for <$10 in compute cost. We demonstrate three distinct workflows across five environments. Direct translation (no prior performance implementation exists): EmuRust (1.5x PPO speedup via Rust parallelism for a Game Boy emulator) and PokeJAX, the first GPU-parallel Pokemon battle simulator (500M SPS random action, 15.2M SPS PPO; 22,320x over the TypeScript reference). Translation verified against existing performance implementations: throughput parity with MJX (1.04x) and 5x over Brax at matched GPU batch sizes (HalfCheetah JAX); 42x PPO (Puffer Pong). New environment creation: TCGJax, the first deployable JAX Pokemon TCG engine (717K SPS random action, 153K SPS PPO; 6.6x over the Python reference), synthesized from a web-extracted specification. At 200M parameters, the environment overhead drops below 4% of training time. Hierarchical verification (property, interaction, and rollout tests) confirms semantic equivalence for all five environments; cross-backend policy transfer confirms zero sim-to-sim gap for all five environments. TCGJax, synthesized from a private reference absent from public repositories, serves as a contamination control for agent pretraining data concerns. The paper contains sufficient detail - including representative prompts, verification methodology, and complete results - that a coding agent could reproduce the translations directly from the manuscript.

Problem

• Reinforcement Learning
• High-Performance Environments
• Automatic Generation
• Semantic Equivalence
• Environment Translation

Innovation

• automatic environment generation
• hierarchical verification
• agent-assisted repair
• GPU-parallel RL simulation
• semantic equivalence