Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies

arXiv:2607.07029v1 Announce Type: new Abstract: 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 desc

Source: arXiv cs.LG — read the full report at the original publisher.

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