Procgen Benchmark Overview

Updated 26 October 2025

Procgen Benchmark is a suite of game-like environments that uses procedural content generation to evaluate RL agents' generalization and sample efficiency.
The benchmark employs standardized evaluation protocols and adjustable difficulty modes to test agent performance under diverse, dynamically generated scenarios.
Empirical studies using Procgen show that larger network architectures converge faster and generalize better across a wide range of randomly generated tasks.

Procgen Benchmark is a suite of 16 procedurally generated game-like environments specifically designed to rigorously evaluate both sample efficiency and, crucially, generalization in deep reinforcement learning (RL). Distinct from static benchmarks such as Arcade Learning Environment, Procgen environments deliver a near-infinite distribution of challenging, game-inspired tasks through procedural content generation, making it possible to quantify and compare how well RL agents acquire skills that generalize beyond memorized solutions.

1. Motivation and Principles

The primary motivation for Procgen Benchmark is to create a controlled experimental platform where the ability of RL agents to generalize—rather than overfit—is directly measurable. In contrast to classic benchmarks with static levels, Procgen leverages procedural content generation (PCG) to provide a continually shifting pool of diverse environments. This ensures that agents must learn policies robust to a wide variety of level configurations, appearances, and dynamics, reducing the risk of overfitting to specific instances. Environments are optimized for high throughput (thousands of steps per second on a single CPU core), uniform action and observation spaces (64×64×3 RGB images, discrete 15-dimensional action space), and support for difficulty tuning via “easy” and “hard” modes, enabling controlled experimentation under varying computational constraints (Cobbe et al., 2019).

2. Environment Suite and Procedural Content Generation

The benchmark’s 16 environments span diverse tasks, including platformers, maze navigation, and combat scenarios, each with richly parameterized procedural logic controlling layouts, asset pools, obstacle and enemy distributions, and game mechanics. Level generation methods range from maze algorithms (e.g., Kruskal’s) and cellular automata (for caves), to random sampling of positions and asset permutations. By continuously sampling from these high-entropy level generators, overfitting to specific trajectories is significantly attenuated; agents face a near-infinite variety of initial states and must infer general strategies.

Environment Aspect	Implementation in Procgen	Significance for RL
Map layout	Randomized algorithms per environment	Ensures diversity
Asset/enemy/obstacle pool	Varied per episode from asset set	Requires adaptability
“Easy/Hard” difficulty	Alters level distribution, not individual	Scalable challenge

3. Sample Efficiency and Generalization Evaluation

Sample efficiency within Procgen is measured by monitoring the episodic return of agents over a fixed step budget (e.g., 25M steps for “easy,” 200M for “hard”). Generalization is formally evaluated by training an agent on a finite set of levels (e.g., 500 or 200) then testing its policy on the full, unseen level distribution. Performance is aggregated via the normalized return metric:

$R_{\text{norm}} = \frac{R - R_{\min}}{R_{\max} - R_{\min}}$

where $R$ is the agent’s return and $R_{\min}, R_{\max}$ are environment-specific constants, mapping raw returns to the [0,1] range for cross-environment comparison. This protocol enables precise quantification and fair ranking of algorithms on both efficiency (learning speed) and generalization (robustness to test distribution) (Cobbe et al., 2019, Mohanty et al., 2021).

4. Experimental Protocols and Baseline Comparisons

The benchmark prescribes rigorous protocols: standardized training durations, fixed train-test splits, unified action and observation spaces, and evaluation via normalized return. PPO is the baseline algorithm, but the suite accommodates arbitrary RL algorithms. During experiments, to isolate generalization, the agent is trained on a limited set of “training” levels and then evaluated on distinct, held-out “test” levels. Sample-efficient methods should attain high return in few steps; generalization-focused methods should maintain high performance under previously unseen configurations.

Large-scale competitions and studies (e.g., NeurIPS 2020 Procgen Competition) have codified these protocols, providing standardized metrics and centralized evaluation setups to ensure direct comparability across diverse methods, architectures, and hyperparameter budgets (Mohanty et al., 2021). Top-performing submissions commonly leverage advances such as enhanced convolutional architectures, intrinsic exploration rewards, mixup/data augmentation, and careful hyperparameter tuning.

5. Scaling Model Capacity and Its Impact

A core empirical result from Procgen is the decisive role of network capacity on both efficiency and generalization. Experiments comparing small (Nature-CNN) and scaled-up (IMPALA-style) convolutional models—by increasing the width (number of channels) up to 4×—demonstrate that larger architectures consistently converge faster and generalize better across environments. The parameter count scales quadratically with width, dramatically increasing representational capability, particularly important for modeling diverse distributions induced by PCG. In contrast, smaller models often fail to converge or display severe overfitting. The effect is robust under fixed learning-rate scaling and supports a broader shift toward high-capacity models for general RL, subject to resource constraints (Cobbe et al., 2019).

6. Research and Methodological Implications

Procgen’s PCG-driven structure has redirected RL research toward methodologies that directly address generalization, representation learning, and sample efficiency. Key implications include:

The necessity of separating metrics for within-distribution (“training”) performance from out-of-distribution (“test”) generalization.
Strong empirical motivation for increased model scale, with corresponding hyperparameter scaling.
Emergence of architectural and algorithmic innovations—such as explicit decoupling of policy and value representation, invariance-based regularization, and style-invariant data bootstrapping—driven by Procgen’s high-diversity content (see, e.g., IDAAC (Raileanu et al., 2021), Thinker (Rahman et al., 2022)).
Emphasis on reliable protocols and normalization (via $R_{\text{norm}}$ ) for robust method comparison and ablation studies.
Direct impact on the design of new benchmarks (e.g., C-Procgen, Craftax), which extend the procedural and contextual flexibility originally pioneered by Procgen.

7. Influence and Outlook

Procgen Benchmark has become a foundational testbed for RL, shaping research agendas around generalization, scalable architectures, and reproducible experimentation. Its influence can be traced in developments such as curriculum learning in context-rich settings (Tan et al., 2023), massively parallelized benchmarks for open-ended RL (Matthews et al., 26 Feb 2024), and scrutiny of model and representation scaling effects (Jesson et al., 13 Oct 2024). The benchmark’s transparent protocols, diverse tasks, and performance normalization contribute to its continued relevance for comparative evaluation and for designing agents and algorithms intended for real-world, variable, and high-diversity domains. A plausible implication is continued evolution toward even richer, context-controllable, and computationally scalable benchmarks, with Procgen serving as a paradigm for the systematic paper of generalization in deep reinforcement learning.