We Automated RL Environment Engineering for $10

arxiv.org · milkkarten · 16 hours ago · view on HN · research
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A research paper demonstrating automated generation of high-performance reinforcement learning environments using LLM-assisted code synthesis with hierarchical verification, achieving 22,320x speedup improvements across multiple environments (Pokemon battle simulator, TCG engine) at minimal compute cost (<$10).

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machine-learning reinforcement-learning code-generation llm-assisted-development performance-optimization jax rust environment-engineering
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Seth Karten Rahul Dev Appapogu Chi Jin EmuRust PokeJAX MJX Brax TCGJax arXiv:2603.12145
[2603.12145] Automatic Generation of High-Performance RL Environments --> Computer Science > Machine Learning arXiv:2603.12145 (cs) [Submitted on 12 Mar 2026] Title: Automatic Generation of High-Performance RL Environments Authors: Seth Karten , Rahul Dev Appapogu , Chi Jin View a PDF of the paper titled Automatic Generation of High-Performance RL Environments, by Seth Karten and Rahul Dev Appapogu and Chi Jin View PDF HTML (experimental) 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. Comments: 26 pages, 9 figures, 8 tables Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Software Engineering (cs.SE) Cite as: arXiv:2603.12145 [cs.LG] (or arXiv:2603.12145v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2603.12145 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Seth Karten [ view email ] [v1] Thu, 12 Mar 2026 16:45:47 UTC (812 KB) Full-text links: Access Paper: View a PDF of the paper titled Automatic Generation of High-Performance RL Environments, by Seth Karten and Rahul Dev Appapogu and Chi Jin View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2026-03 Change to browse by: cs cs.AI cs.SE References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) Links to Code Toggle Papers with Code ( What is Papers with Code? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? )
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