OpenAI Gym：强化学习的基准测试工具

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"OpenAI Gym 是一个专为强化学习研究设计的工具包，提供了丰富的基准测试问题，具有统一的接口，并有一个平台供研究者展示和比较不同算法的性能。此工具由 Greg Brockman、Vicki Cheung、Ludwig Pettersson、Jonas Schneider、John Schulman、Jie Tang 和 Wojciech Zaremba 等人开发，由 OpenAI 组织发布。" OpenAI Gym 是一个重要的强化学习平台，它的核心功能和设计目标在于促进 RL 算法的研发和比较。强化学习是机器学习的一个分支，关注的是在动态环境中做出一系列决策的过程。RL 的理论基础深厚，并已在许多实际应用中展现出价值。近年来，深度学习与强化学习的结合带来了显著的突破，例如，政策梯度和 Q 学习等通用算法在解决复杂问题时表现出色，无需针对特定问题进行大量工程优化。为了推动强化学习领域的进一步发展，研究者需要一套标准化的基准测试环境来评估和比较他们的算法。OpenAI Gym 正是为了满足这一需求而创建的，它包含了一系列不断增长的环境（或称为“健身房”），这些环境覆盖了各种各样的任务，从简单的控制问题到复杂的模拟环境。这些环境都遵循一个统一的 API，使得研究人员可以轻松地在不同的环境中测试和比较算法的性能。 OpenAI Gym 的主要组件包括： 1. **环境（Environments）**：这是构成 Gym 的基础，涵盖了各种各样的强化学习问题，如 Atari 游戏、经典的控制问题（如倒立摆）、棋类游戏（如围棋、国际象棋）以及更复杂的模拟环境（如 MuJoCo）。每个环境都提供了一个 `step()` 函数，用于执行一个动作并返回新的状态、奖励和是否结束的信息。 2. **接口（Interface）**：Gym 提供了一个简洁一致的 Python 接口，允许用户轻松地初始化环境、获取环境信息、执行动作和观察结果。这种标准化的接口极大地简化了算法的跨环境评估。 3. **结果分享和比较平台**：OpenAI Gym 的网站允许研究者上传他们的算法在不同环境下的性能数据，这样其他人可以查看并比较不同算法的表现，促进了社区内的合作和竞争。 4. **库和工具**：除了基本的环境，Gym 还提供了一些辅助库和工具，如记录和可视化工具，帮助研究人员更好地理解和分析实验结果。 5. **开源社区**：OpenAI Gym 是一个开源项目，其持续发展得益于全球开发者社区的贡献，这意味着它能够快速适应新出现的研究需求和挑战。通过 OpenAI Gym，研究者可以专注于算法的设计和改进，而不必花费大量时间在环境的搭建和标准化上。此外，Gym 的存在鼓励了算法的可重复性和透明度，这对于科学进步至关重要。未来，随着更多环境的加入和社区的不断壮大，OpenAI Gym 将继续推动强化学习领域的创新和进展。

OpenAI Gym

Greg Brockman, Vicki Cheung, Ludwig Pettersson,

Jonas Schneider, John Schulman, Jie Tang, Wojciech Zaremba

OpenAI

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.

1 Introduction

Reinforcement learning (RL) is the branch of machine learning that is concerned with making sequences of

decisions. RL has a rich mathematical theory and has found a variety of practical applications [1]. Recent

advances that combine deep learning with reinforcement learning have led to a great deal of excitement

in the ﬁeld, as it has become evident that general algorithms such as policy gradients and Q-learning can

achieve good performance on difﬁcult problems, without problem-speciﬁc engineering [2, 3, 4].

To build on recent progress in reinforcement learning, the research community needs good benchmarks

on which to compare algorithms. A variety of benchmarks have been released, such as the Arcade Learn-

ing Environment (ALE) [5], which exposed a collection of Atari 2600 games as reinforcement learning

problems, and recently the RLLab benchmark for continuous control [6], to which we refer the reader for

a survey on other RL benchmarks, including [7, 8, 9, 10, 11]. OpenAI Gym aims to combine the best el-

ements of these previous benchmark collections, in a software package that is maximally convenient and

accessible. It includes a diverse collection of tasks (called environments) with a common interface, and this

collection will grow over time. The environments are versioned in a way that will ensure that results remain

meaningful and reproducible as the software is updated.

Alongside the software library, OpenAI Gym has a website (gym.openai.com) where one can ﬁnd score-

boards for all of the environments, showcasing results submitted by users. Users are encouraged to provide

links to source code and detailed instructions on how to reproduce their results.

2 Background

Reinforcement learning assumes that there is an agent that is situated in an environment. Each step, the agent

takes an action, and it receives an observation and reward from the environment. An RL algorithm seeks to

maximize some measure of the agent’s total reward, as the agent interacts with the environment. In the RL

literature, the environment is formalized as a partially observable Markov decision process (POMDP) [12].

OpenAI Gym focuses on the episodic setting of reinforcement learning, where the agent’s experience

is broken down into a series of episodes. In each episode, the agent’s initial state is randomly sampled

from a distribution, and the interaction proceeds until the environment reaches a terminal state. The goal in

episodic reinforcement learning is to maximize the expectation of total reward per episode, and to achieve a

high level of performance in as few episodes as possible.

The following code snippet shows a single episode with 100 timesteps. It assumes that there is an object

called agent, which takes in the observation at each timestep, and an object called env, which is the

gym.openai.com

arXiv:1606.01540v1 [cs.LG] 5 Jun 2016

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