Gimitest是测试强化学习策略的开源工具，支持多环境与多智能体测试。
Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies
- 构建通用框架，适配多种gym环境与RL算法。
- 在Farama Gymnasium和PettingZoo中验证了对多个策略的有效性。
- 支持组件自定义，适合研究与工程中的安全测试。
强化学习策略可能存在安全隐患且易受攻击。现有自动化测试方法仅针对特定环境、测试场景和算法，难以保障可靠性。为此，我们提出一个通用框架，用于在不同条件下测试单智能体与多智能体强化学习策略。该框架的实现Gimitest是一个开源工具，支持多种gym框架，并可修改其集成组件。本文介绍该框架设计与Gimitest的功能架构，并通过Farama Gymnasium和PettingZoo等环境验证其有效性，展示了对多个强化学习策略的测试能力。
原文摘要 · Abstract (English)
Reinforcement learning (RL) policies can be unsafe and vulnerable to attacks. Ensuring their reliability is often a pain point as existing automated testing methods target only selected environments, testing scenarios, and RL algorithms. To address this, we propose a comprehensive framework for testing single- and multi-agent RL policies under varying conditions. Our implementation of this framework, Gimitest, is an open-source tool that supports various gym frameworks and allows for modifications of their integrated components. This article describes the framework and details Gimitest's functionality and architecture. It showcases its effectiveness in testing multiple RL policies in environments such as the official Farama Gymnasium and PettingZoo.
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