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Leveraging Procedural Generation to Benchmark Reinforcement Learning
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We introduce Procgen Benchmark, a suite of 16 procedurally generated game-like environments designed to benchmark both sample efficiency and generalization in reinforcement learning. We believe that the community will benefit from increased access to high quality training environments, and we provide detailed experimental protocols for using this benchmark. We empirically demonstrate that diverse environment distributions are essential to adequately train and evaluate RL agents, thereby motivating the extensive use of procedural content generation. We then use this benchmark to investigate the effects of scaling model size, finding that larger models significantly improve both sample efficiency and generalization.
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Cited by 6 Pith papers
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Reasoning Core: Designing Broad Procedural Data for Completion-Supervised Reasoning Training
A new collection of 50 procedural generators designed for completion-supervised fine-tuning beats three existing procedural collections and a no-procedural baseline on reasoning benchmarks at 3B scale in mean scores.
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Dyn-O: Building Structured World Models with Object-Centric Representations
Dyn-O learns object-centric world models directly from pixels in complex Procgen games, using SAM2-guided slot attention and Mamba state-space dynamics, and reports better rollout prediction than DreamerV3.
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