Procgen Benchmark

Procgen Benchmark
Name	Procgen Benchmark
Developer	OpenAI
Released	2019
Platform	Linux, macOS, Windows
Genre	Reinforcement learning benchmark
License	MIT License

Procgen Benchmark

The Procgen Benchmark is a suite of procedurally generated environments for evaluating reinforcement learning agents developed by OpenAI. It was created to measure sample efficiency, generalization, and robustness across diverse simulated tasks used in contemporary machine learning research, aligning with interests from institutions such as OpenAI, DeepMind, Google Research, Facebook AI Research, and Microsoft Research.

Overview

The benchmark provides a standardized set of environments inspired by prior work from groups like Stanford University and MIT labs, enabling comparisons across algorithms from University of California, Berkeley, Carnegie Mellon University, ETH Zurich, University of Toronto, and industry groups including NVIDIA and Intel Labs. It leverages procedural generation techniques related to projects at Unity Technologies, Epic Games, Mozilla Foundation, and research initiatives tied to DARPA, NSF, and European Research Council grants. The benchmark influenced evaluations in papers presented at conferences such as NeurIPS, ICLR, ICML, AAAI, and AAMAS.

Benchmark Design and Methods

Design choices draw upon methodologies from classical control benchmarks like OpenAI Gym and simulation frameworks including ALE and MuJoCo that originated in collaborations with teams at University of Toronto and University of Texas at Austin. Procedural generation algorithms in the suite reflect techniques employed in game development studios such as Blizzard Entertainment, Valve Corporation, Riot Games, and research labs at Unity Labs. Experimental protocols recommended by the benchmark mirror evaluation practices used in publications affiliated with Berkeley AI Research and Oxford University groups, and follow reproducibility standards promoted by ACM and IEEE.

Environments and Tasks

The environments cover navigational, platformer, and collection tasks informed by prior benchmarks like VizDoom, Doom modding communities, and procedurally generated game environments used at Carnegie Mellon University and University College London. Specific task categories reflect influences from projects such as StarCraft II research from DeepMind and Blizzard Entertainment, multi-agent work tied to Google DeepMind Control Suite, and robotic benchmarks developed by MIT CSAIL and Stanford Robotics Lab. The suite’s diversity aligns with evaluation needs emphasized at conferences like KDD and SIGGRAPH where procedural content generation and simulation fidelity are discussed.

Evaluation Metrics and Baselines

The benchmark recommends metrics for generalization and sample efficiency similar to those used in studies from DeepMind and OpenAI submitted to ICLR and NeurIPS. Baselines frequently cited include algorithms from teams at Google Brain, DeepMind, OpenAI, Berkeley AI Research, and implementations originating in repositories maintained by GitHub organizations and contributors from Cornell University. Statistical analysis techniques parallel standards advocated by American Statistical Association and evaluation checklists used at NeurIPS and ICML reproducibility tracks.

Results and Findings

Published results demonstrated that agents developed by groups at OpenAI, DeepMind, Google Research, Facebook AI Research, and university labs such as University of California, Berkeley, Carnegie Mellon University, University of Oxford, and ETH Zurich show varying degrees of overfitting to training distributions. Papers presented at ICLR, NeurIPS, and ICML reported that approaches combining representation learning from teams at Stanford University and exploration methods popularized by Google DeepMind tended to generalize better. Follow-up studies influenced work at industrial research groups including Amazon AI and academic consortia funded by European Research Council grants.

Impact and Applications

The benchmark informed research directions at institutions like OpenAI, DeepMind, Google Research, Microsoft Research, Facebook AI Research, and university labs at MIT, Stanford University, Carnegie Mellon University, ETH Zurich, and University of Cambridge. It has been used to evaluate algorithms adapted for robotics efforts at Boston Dynamics, autonomous systems research at Waymo, and simulation platforms developed by NVIDIA and Unity Technologies. Results influenced curricula and projects at universities such as Harvard University, Princeton University, and Caltech and were cited in grant proposals to agencies like NSF and DARPA.

Limitations and Future Work

Limitations noted by research teams at OpenAI, DeepMind, Google Research, and university collaborators include gaps in realism compared to simulators from MuJoCo and game engines from Epic Games and Unity Technologies, and constraints recognized in reproducibility discussions at NeurIPS and ICLR. Future work proposed by authors affiliated with Berkeley AI Research, Stanford AI Lab, MIT CSAIL, and ETH Zurich emphasizes integrating richer physics from NASA-affiliated projects, multi-agent extensions inspired by Stanford Multi-Agent Systems Lab, and benchmarking standards advocated by ACM and IEEE committees.

Category:Reinforcement learning benchmarks