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Leveraging Procedural Generation to Benchmark Reinforcement Learning
Karl Cobbe, Christopher Hesse, Jacob Hilton, John Schulman
TL;DR
Reinforcement learning lacks environments that adequately measure generalization across diverse tasks. The paper introduces Procgen Benchmark, a suite of procedurally generated environments with protocols for evaluating sample efficiency and generalization, and finds that diverse distributions and larger models improve these capabilities.
Problem
Existing reinforcement-learning environments and studies provide limited evidence about generalization because agents can overfit and few environments support broad conclusions.
Method
The paper introduces 16 procedurally generated environments with distinct training and test distributions and protocols for measuring sample efficiency and generalization.
Results
Agents overfit small training sets, diverse environment distributions are essential for adequate training and evaluation, and larger architectures improve sample efficiency and generalization.
Takeaways & Limitations
Procgen Benchmark provides a practical tool for studying robust policies, generalization, and sample efficiency across diverse reinforcement-learning environments.
Takeaways & Limitations
Rainbow has low performance in some environments, which the authors identify as an avenue for further research.
Abstract
from arXiv · showhide
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.
1. Introduction
Generalization is a central challenge in reinforcement learning because agents can overfit large training sets, while existing environments provide too little basis for broad conclusions. Procgen Benchmark addresses this gap with diverse environments that require generalization and support separate training and test sets.
- Generalization remains a fundamental reinforcement-learning challenge because agents can overfit remarkably large training sets.
- Existing studies reveal intriguing generalization trends, but their small number of environments makes broad conclusions difficult.
- Procgen Benchmark combines diversity comparable to ALE with environments that fundamentally require generalization.
- Distinct training and test sets make Procgen suitable for evaluating generalization, while diverse challenges also support sample-efficiency evaluation.
2. Procgen Benchmark
Procgen Benchmark provides 16 procedurally generated environments with diverse level distributions, shared interfaces, tunable difficulty, and protocols for evaluating generalization and sample efficiency. Procedural variation spans layouts, assets, entities, and other game details, requiring policies robust across multiple axes of variation.
- Procgen Benchmark consists of 16 unique environments designed to measure sample efficiency and generalization in reinforcement learning.
- Procedural generation varies layouts, assets, entity locations, spawn times, and other game-specific details, requiring policies robust across all variation axes.
- The benchmark emphasizes high diversity, with level distributions designed to present meaningful generalization challenges.
- Environment Desiderata: Each environment supports easy and hard difficulty settings, with easy difficulty reducing training resources by roughly a factor of 8.
- All environments share a discrete 15-dimensional action space and 64 × 64 × 3 RGB observations for a unified training pipeline.
- Experimental Protocols: For generalization evaluation, agents train on finite level sets and test on the full level distribution, typically using 500 training levels.
3. Generalization Experiments
The experiments show that agents overfit small training sets and require highly diverse training and evaluation distributions for reliable generalization. A deterministic-level ablation exposes a large train–test discrepancy, while the benchmark provides a practical 500-level generalization protocol.
- Level Requirements: Agents strongly overfit small training sets in almost all environments and may require as many as 10,000 levels to close the generalization gap.
- Level Requirements: Larger training sets can improve training performance beyond a threshold, which the authors attribute to an implicit curriculum from level distributions.
- An Ablation with Deterministic Levels: Training on fixed level sequences can produce apparent competence, while random-sequence testing shows agents learned almost nothing about the underlying level distribution.
- An Ablation with Deterministic Levels: These results emphasize training and evaluating agents with diverse environment distributions.
- 500 Level Generalization: The recommended generalization protocol trains on 500 levels and measures zero-shot performance averaged over unseen levels.
- 500 Level Generalization: Baseline evaluations show substantial overfitting in most environments, while small generalization gaps can coincide with poor training and test performance.
4. Scaling Model Size
The benchmark is used to test how model size affects sample efficiency and generalization. Larger architectures improve both, while the small Nature-CNN nearly fails to train.
- Four models are evaluated on both sample efficiency and generalization using the experimental protocols described for Procgen Benchmark.The first three use scaled IMPALA architectures; the fourth uses Nature-CNN.
- Scaling IMPALA channels by 1, 2, or 4 increases parameter count by approximately k2 while varying the learning rate accordingly.The learning rate is the only hyperparameter varied between architectures.
- Larger architectures significantly improve both sample efficiency and generalization across diverse environments.The authors report that some environments benefit more strongly than others.
- The small Nature-CNN model almost completely fails to train.
5. Comparing Algorithms
The paper compares PPO and Rainbow on the full level distributions of Procgen environments. PPO performs more consistently, while Rainbow substantially improves performance in several environments but remains unstable in others.
- PPO and Rainbow are trained and tested on the full distribution of levels in each environment.Rainbow uses the IMPALA convolutional architecture and experience from 64 parallel environment copies.
- Increasing Rainbow’s batch size and learning rate while reducing update frequency significantly reduced wall-clock training time without adversely impacting performance.Each of the batch size and update frequency changes was scaled by a factor of 16, while the learning rate was increased by a factor of 4.
- PPO performs much more consistently across the benchmark than Rainbow.
- Rainbow offers a significant improvement in several environments.
- Rainbow’s low performance in some environments is associated with instability that the authors cannot presently diagnose.
6. Related Work
Related benchmarks study RL generalization through level splits, procedural generation, randomization, or novel tasks. Procgen instead emphasizes many heterogeneous environments to measure generalization across diverse settings.
- Sonic separates game levels into training and test sets, but agents struggled to generalize from its few available training levels.
- CoinRun uses procedurally generated training and test sets to address limitations associated with small training sets.
- Prior procedurally generated environments found strong overfitting to particular training sets across different level distributions.Procgen environments are designed in a similar spirit, with Miner and Leaper drawing directly from that work.
- Obstacle Tower studies generalization in one complex 3D environment spanning visual control and planning, whereas Procgen uses many heterogeneous environments.
- bsuite targets a small number of core RL capabilities, while visual generalization is a primary difficulty across all Procgen environments.
- Safety Gym uses extensive randomization to prevent overfitting to specific layouts, thereby enforcing a need for generalization.
- Animal-AI evaluates generalization from training configurations to unseen tests within a single unified environment.
- Meta-World uses up to 50 continuous-control environments for training and testing, with shared physics and mechanics supporting plausible generalization.
7. Conclusion
Procgen Benchmark addresses reinforcement learning’s generalization challenge with diverse, procedurally generated environments designed to evaluate both generalization and sample efficiency. Its level distributions create exploration and robustness challenges across varied game layouts, assets, entities, and obstacles.
- 7. Conclusion: Procgen Benchmark targets generalization and sample efficiency across 16 unique procedurally generated environments.The benchmark uses algorithmic generation to provide highly randomized content rather than relying on fixed, human-designed levels.
- 7. Conclusion: Procgen environments are intended to support fast experimental pipelines and future work on more capable and efficient reinforcement-learning algorithms.The benchmark’s intrinsic diversity is presented as useful for evaluating generalization and sample efficiency.
- 7. Conclusion: Procedural generation varies level layouts, game assets, entity locations and spawn times, and other game-specific details.Agents must learn policies robust across these axes of variation.
- 7. Conclusion: The environments include distinct generation mechanisms such as cellular automata, recursive room construction, maze generation, and randomized obstacle or entity configurations.These mechanisms produce diverse layouts and gameplay conditions across environments.
- 7. Conclusion: Training performance often increases with training-set size, while restricting diversity can make otherwise tractable environments intractable because of exploration.Restricting training to one high-difficulty level turns 8 environments into challenging exploration tasks, without measuring generalization in that setting.
E. Test Performance for All Training Sets
This section presents test-performance evaluation across agents trained on different level sets, with performance assessed over complete environment level distributions. It also includes the PPO-on-ALE comparison and notes the frame-stack and rendering settings used in the ALE experiments.
- E. Test Performance for All Training Sets: Agents trained on different sets of levels are evaluated on the full distribution of levels from each environment.Figure 7 describes the test-performance comparison and evaluation distribution.
- E. Test Performance for All Training Sets: Figure 8 reports the performance of the authors’ PPO implementation on the Arcade Learning Environment.The supplied passage does not state a numerical outcome.
- E. Test Performance for All Training Sets: The ALE experiments use a frame stack of 4 and omit sticky actions.Procgen is rendered at 64x64 pixels but can also be rendered at 84x84 pixels to match the ALE standard.
G. Training Curves by Architecture
This section compares architecture-specific performance under two evaluation settings: training and testing on the full level distribution, and training on 500 levels with evaluation on held-out levels.
- G. Training Curves by Architecture: Figure 9 compares agents using different architectures in each environment when trained and evaluated on the full distribution of levels.The supplied caption does not report a numerical performance result.
- G. Training Curves by Architecture: Figure 10 compares architecture-specific training and test curves for agents trained on 500 levels and evaluated on held-out levels.Light dashed lines denote training curves, while dark solid lines denote test curves.
H. Frame Stack vs. LSTM
The paper compares its single-frame baseline with four-frame stacking and an LSTM over the convolutional network. These methods are generally comparable, with environment-specific advantages and instability for the LSTM.
- H. Frame Stack vs. LSTM: The baseline processes only the current frame, while comparison agents use a frame stack of 4 or an LSTM atop the convolutional network.The comparison examines alternative ways to incorporate temporal information.
- H. Frame Stack vs. LSTM: In Jumper, LSTM agents outperform the other methods, possibly because temporally extended navigation is useful there.This is presented as a possible explanation specific to the Jumper environment.
- H. Frame Stack vs. LSTM: In Leaper and Ninja, the LSTM baseline is notably unstable, whereas frame-stack agents are usually similar to the single-frame baseline.Frame stacking produces noticeable differences in only a few environments.
I. Easy Difficulty Baseline Results
The section presents agent performance on easy-difficulty environments under two training and evaluation protocols: full-distribution training versus training on 200 levels.
- Figure 12 reports agent performance on easy-difficulty environments when agents are trained and evaluated on the full distribution of levels.
- Together, the figures organize the easy-difficulty results around whether training uses the full level distribution or 200 levels, while evaluation uses the full distribution in both cases.
- Figure 13 reports agent performance on easy-difficulty environments when agents are trained on 200 levels and evaluated on the full distribution of levels.