[Paper Review] OpenAI Gym

Greg Brockman, Vicki Cheung+5 more|arXiv (Cornell University)|Jun 5, 2016

Educational Games and Gamification632 citations

TL;DR

OpenAI Gym is a reinforcement learning benchmarking toolkit that provides a library of environments with a common interface and a website to share and compare results.

ABSTRACT

OpenAI Gym is a toolkit for reinforcement learning research. It includes a growing collection of benchmark problems that expose a common interface, and a website where people can share their results and compare the performance of algorithms. This whitepaper discusses the components of OpenAI Gym and the design decisions that went into the software.

Motivation & Objective

• Provide a convenient, extensible collection of RL environments with a common interface.
• Enable reproducible benchmarking by versioning environments and monitoring training data.
• Encourage sharing of code, results, and reproducibility through a community scoreboard and Writeups.
• Balance emphasis on sample efficiency and final performance in evaluating RL algorithms.

Proposed method

• Define environments as the core abstraction, excluding a fixed agent interface to accommodate different agent styles.
• Instrument environments with a Monitor to record steps, resets, and optionally video data for learning curves.
• Version environments strictly to ensure results remain meaningful across updates (e.g., CartPole-v0 to CartPole-v1).
• Offer a diverse set of environments including classic control, algorithmic tasks, Atari games via ALE, board games, and robotics simulators (MuJoCo, Box2D, VizDoom).
• Provide a website with scoreboards where users can submit results, source code links, and reproduction instructions.

Experimental results

Research questions

• RQ1 How can a common interface across diverse RL tasks facilitate fair comparison of algorithms?
• RQ2 What design decisions best support reproducibility and meaningful benchmarking over time?
• RQ3 Can a community-driven platform effectively balance learning progress, final performance, and resource usage in RL benchmarks?
• RQ4 How should environments be versioned and monitored to prevent overfitting to specific tasks or versions?

Key findings

• A unified environment-centric framework supports various RL problems while remaining flexible for different agent interfaces.
• Versioning and monitoring are central to ensuring reproducible and interpretable benchmarking results.
• A diverse suite of environments is provided, spanning classic control, algorithms, Atari, board games, and robotics simulations.
• The platform emphasizes sharing code and writeups to aid reproducibility rather than competing for leaderboard supremacy.