OpenAI Gym Beta Release

OpenAI has released the public beta of OpenAI Gym, a toolkit for developing and comparing reinforcement learning (RL) algorithms. This release provides a standardized suite of environments and a platform for reproducing and comparing research results, addressing the lack of consistency and variety in RL benchmarks.

Standardizing Reinforcement Learning Benchmarks

OpenAI Gym aims to solve two primary bottlenecks in RL research: the lack of diverse, easy-to-use benchmarks and the lack of standardization in environment definitions. In supervised learning, progress is driven by large labeled datasets like ImageNet; OpenAI Gym provides a similar function for RL by offering a diverse collection of environments, ranging from simulated robots to Atari games.

Standardization is critical because subtle changes in reward functions or action sets can significantly alter the difficulty of a task. By providing a consistent set of environments, OpenAI Gym enables researchers to more accurately reproduce published research and compare results across different papers.

Toolkit Capabilities and Compatibility

OpenAI Gym is designed to be framework-agnostic, making it compatible with algorithms written in any framework, such as TensorFlow and Theano. While the environments are currently written in Python, OpenAI intends to make them accessible from other programming languages in the future.

Approach to Evaluation and Results

OpenAI Gym avoids traditional leaderboards to discourage overfitting or the creation of hand-crafted solutions for specific tasks. Instead, the focus is placed on the generality of the technique being developed.

To track progress, OpenAI is maintaining a curated list of contributions that demonstrate interesting algorithmic capabilities. The long-term goal is for this curation process to be a community-driven effort rather than a centralized process owned by OpenAI.