Automatic Generation of High-Performance RL Environments
Translating complex reinforcement learning (RL) environments into high-performance implementations has traditionally required months of specialized engineering. We present a closed-loop methodology that produces equivalent high-performance environments for minimal compute cost. Our method uses a generic prompt template, hierarchical verification (property, interaction, and rollout tests), iterative repair, and cross-backend policy transfer to verify no sim-to-sim gap. We demonstrate three distinct workflows across five environments: (1) Direct translation (no prior performance implementation exists) from Game Boy emulator PyBoy to our EmuRust (via Rust IPC) and from Pokemon Showdown to ou...
| Authors | Seth Karten, Rahul Dev Appapogu, Chi Jin |
|---|---|
| Primary category | cs.LG |
| Published | 12 March 2026 |
| Last updated at source | 17 May 2026 |
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