Procgen Benchmark: Measuring Generalization in Reinforcement Learning
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About this episode
The 2019 OpenAI Procgen Benchmark is a suite of 16 procedurally generated environments created to measure the **generalization and sample efficiency** of reinforcement learning agents. Unlike traditional benchmarks with fixed layouts, these games use **algorithmic randomization** to ensure agents develop robust skills rather than simply memorizing specific trajectories. Research using this tool reveals that **diversified training sets** are vital for performance, as agents often overfit when exposed to limited levels. Findings also indicate that **increasing model size** significantly boosts an agent's ability to adapt to novel visual challenges and complex motor tasks. By providing **high-speed, diverse simulations**, the benchmark offers a rigorous standard for evaluating how well autonomous systems transfer knowledge to unseen scenarios.
Sources:
1)
December 3, 2019
Procgen Benchmark
OpenAI
Karl Cobbe, Christopher Hesse, Jacob Hilton, John Schulman
https://openai.com/index/procgen-benchmark/
2)
2020
Leveraging Procedural Generation to Benchmark Reinforcement Learning
OpenAI
Karl Cobbe, Christopher Hesse, Jacob Hilton, John Schulman
https://arxiv.org/pdf/1912.01588
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