Procgen Benchmark

# Procgen Benchmark Source: [https://openai.com/index/procgen-benchmark/](https://openai.com/index/procgen-benchmark/) [In⁠\(opens in a new window\)](https://arxiv.org/abs/1804.06893)[several⁠](https://openai.com/index/quantifying-generalization-in-reinforcement-learning/)[environments⁠\(opens in a new window\)](https://arxiv.org/abs/1806.10729), it has been observed that agents can overfit to remarkably large training sets\. This evidence raises the possibility that overfitting pervades classic benchmarks like the[Arcade Learning Environment⁠\(opens in a new window\)](https://arxiv.org/abs/1207.4708), which has long served as a gold standard in reinforcement learning \(RL\)\. While the diversity between different games in the ALE is one of the benchmark’s greatest strengths, the low emphasis on generalization presents a significant drawback\. In each game the question must be asked: are agents robustly learning a relevant skill, or are they approximately memorizing specific trajectories? [CoinRun⁠](https://openai.com/index/quantifying-generalization-in-reinforcement-learning/)was designed to address precisely this issue, by using procedural generation to construct distinct sets of training levels and test levels\. While CoinRun has helped us better quantify generalization in RL, it is still only a single environment\. It’s likely that CoinRun is not fully representative of the many challenges RL agents must face\. We want the best of both worlds: a benchmark comprised of many diverse environments, each of which fundamentally requires generalization\. To fulfill this need, we have created Procgen Benchmark\. CoinRun now serves as the inaugural environment in Procgen Benchmark, contributing its diversity to a greater whole\. Previous work, including the[Obstacle Tower Challenge⁠\(opens in a new window\)](https://arxiv.org/abs/1902.01378)and the[General Video Game AI framework⁠\(opens in a new window\)](https://arxiv.org/abs/1802.10363), has also encouraged using procedural generation to better evaluate generalization in RL\. We’ve designed environments in a similar spirit, with two Procgen environments drawing direct inspiration from[GVGAI\-based work⁠\(opens in a new window\)](https://arxiv.org/abs/1806.10729)\. Other environments like Dota and StarCraft also provide lots of per\-environment complexity, but these environments are hard to rapidly iterate with \(and it’s even harder to use more than one such environment at a time\)\. With Procgen Benchmark, we strive for all of the following: experimental convenience, high diversity within environments, and high diversity across environments\. We found that agents strongly overfit to small training sets in almost all environments\. In some cases, agents need access to as many as 10,000 levels to close the generalization gap\. We also saw a peculiar trend emerge in many environments: past a certain threshold, training performance improves as the training sets grows\! This runs counter to trends found in supervised learning, where training performance commonly decreases with the size of the training set\. We believe this increase in training performance comes from an implicit curriculum provided by a diverse set of levels\. A larger training set can improve training performance if the agent learns to generalize*even across levels in the training set*\. We previously noticed this effect with CoinRun, and have found it often occurs in many Procgen environments as well\.