Source-linked AI summary
OpenAI Gym
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, Wojciech Zaremba
TL;DR
Reinforcement learning research needs accessible, shared benchmarks for comparing algorithms. OpenAI Gym addresses this with common-interface environments and a results-sharing website, providing scoreboards and reproducibility information.
Problem
Reinforcement learning research needs convenient, diverse benchmarks with common interfaces for comparing algorithms.
Method
The paper presents a software package of versioned environments with a common interface and a website for sharing algorithm results.
Results
OpenAI Gym provides diverse reinforcement learning tasks, environment scoreboards, and user-submitted links and instructions for reproducing results.
Takeaways & Limitations
The toolkit supports standardized evaluation and public comparison of reinforcement learning algorithms across shared environments.
Takeaways & Limitations
The initial tasks are intended to be solved from scratch, while curriculum and transfer-learning sequences are left for future work.
Abstract
from arXiv · showhide
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.
1 Introduction
Recent advances in deep reinforcement learning have increased excitement around general algorithms, creating a need for good benchmarks to compare them. OpenAI Gym addresses this need through a benchmark library and a website for sharing and reproducing results.
- Motivation: Deep reinforcement learning has shown that general methods such as policy gradients and Q-learning can perform well on difficult problems without problem-specific engineering.These advances have generated substantial excitement in reinforcement learning.
- Benchmarking: OpenAI Gym aims to provide good benchmarks for comparing reinforcement learning algorithms, building on prior collections such as ALE and RLLab.The paper positions Gym as combining strengths from existing reinforcement-learning benchmarks.
- Results sharing: The Gym website provides environment scoreboards where users can submit results, source-code links, and detailed reproduction instructions.The site is available at gym.openai.com.
2 Background
Reinforcement learning models an agent interacting with an environment to maximize total reward, while OpenAI Gym focuses on episodic interactions that end at terminal states. Gym provides the environment-side interface and leaves the agent’s class and interface unspecified.
- Reinforcement learning setting: Reinforcement learning models an agent taking actions and receiving observations and rewards from an environment, formalized as a partially observable Markov decision process.The algorithm seeks to maximize a measure of the agent’s total reward during interaction.
- Episodic setting: OpenAI Gym focuses on episodic reinforcement learning, where each episode begins from a randomly sampled initial state and ends at a terminal state.The objective is to maximize expected total reward per episode while achieving high performance in as few episodes as possible.
- Episode interaction: A single demonstrated episode runs for 100 timesteps, with the agent acting on observations and the environment returning observations, rewards, and completion flags.The example ends when the terminal-state flag is true at timestep 100.
- Episode interaction: OpenAI Gym does not include an agent class or specify the interface an agent should use; the example agent is included only for demonstration.The environment object supplies the interaction methods used in the example.
3 Design Decisions
OpenAI Gym’s design decisions prioritize flexible environment abstractions, evaluation of both final performance and sample complexity, peer review, reproducibility through versioning, and default monitoring.
- Environments, not agents: Gym provides an abstraction for environments, not agents, maximizing user convenience and supporting different agent-interface styles.This allows users to implement alternative agent interfaces, including online-learning and batch-update styles.
- Emphasize sample complexity, not just final performance: RL evaluation should consider both final performance and sample complexity rather than final performance alone.Final performance is average reward per episode after learning; sample complexity measures the time required to learn, such as episodes before reaching a performance threshold.
- Encourage peer review, not competition: Gym scoreboards aim to stimulate sharing of code and ideas and provide meaningful benchmarks, not create competition.The website allows users to compare algorithm performance, while its stated purpose differs from contest-oriented leaderboards.
- Strict versioning for environments: Environment changes must increase the version number so results from different environment versions remain comparable.For example, a changed CartPole environment would move from Cartpole-v0 to Cartpole-v1.
- Monitoring by default: By default, Gym instruments environments with a Monitor that records every simulation step and reset.The configurable Monitor can periodically record video and produce learning curves, with data easily posted to the Gym website.
4 Environments
OpenAI Gym provides a growing collection of POMDP environments spanning classic RL tasks, computation, games, and simulated robotics. Its initial beta release included several environment categories, with additional Box2D- and VizDoom-based environments added later.
- Classic control and toy text environments provide small-scale tasks from the reinforcement-learning literature.
- Algorithmic environments test computations such as multi-digit addition and sequence reversal, with difficulty controlled by sequence length.Most tasks require memory.
- Atari environments use screen images or RAM as input and are built with the Arcade Learning Environment.
- Board-game environments include Go on 9x9 and 19x19 boards, with the Pachi engine serving as opponent.
- Robot environments simulate 2D and 3D control using MuJoCo, while later additions included environments based on Box2D and VizDoom.Some robot tasks were adapted from RLLab.
5 Future Directions
OpenAI Gym’s future extensions target multi-agent tasks, sequentially structured curriculum and transfer learning, and validation on robotic hardware in real-world operation.
- Multi-agent setting: Future Gym tasks may require agents to collaborate or compete with other agents.
- Curriculum and transfer learning: Future curriculum and transfer-learning settings will sequence increasingly difficult tasks that algorithms solve in order rather than from scratch.Current tasks are intended to be solved from scratch; future sequences train algorithms on one task after another.
- Real-world operation: The Gym API may eventually integrate with robotic hardware to validate reinforcement-learning algorithms in the real world.