[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83175-en":3,"doc-seo-83175-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},83175,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies","Reinforcement learning (RL) policies can be unsafe and susceptible to adversarial attacks, and current automated testing approaches often cover only limited environments, scenarios, or algorithms. A comprehensive framework is proposed to test single- and multi-agent RL policies under varying conditions. The implementation, Gimitest, is an open-source tool for multiple gym ecosystems with configurable modifications to integrated components. The work details framework functionality and architecture and demonstrates effectiveness across environments including Farama Gymnasium and PettingZoo.","arXiv :2607 .07029v 1 [ cs .LG] 8 Jul 2026  \nGimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies  \nDennis Gross Quentin Mazouni Helge Spieker Arnaud Gotlieb  \nSimula Research Laboratory, Oslo, Norway  \n[dennis@artigo. ai](dennis@artigo. ai)[ ](dennis@artigo. ai)[quentin@simula. no](quentin@simula. no)[ ](quentin@simula. no)[helge@simula. no](helge@simula. no)[ ](helge@simula. no)[arnaud@simula. no](arnaud@simula. no)  \nAbstract  \nReinforcement 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.  \nThis 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.  \n1 Introduction  \nReinforcement Learning (RL) has improved various industries (Cui & Yuan, 2024; Tian et al., 2024; Zhou et al., 2023), enabling the creation of agents that can outperform humans in sequential decision-making tasks (Mnih et al., 2015) .  \nGenerally, RL learns a near-optimal policy to achieve a fixed objective by acting and receiving rewards and observations from an environment simulator (see Example 1 and Figure 1) (Mnih et al., 2013) . The RL setting can include a single agent (Arulkumaran et al., 2017) or multiple agents (MARL) (Zhu et al., 2024) that interact with the environment in parallel (see Figure 2) or in a turn-based manner (see Figure 3) .  \n RL Agent π (o)  a  \no = O (s), rew  Environment   \nFigure 1: Single-agent RL system, where an agent receives an observation o and a reward rew from the environment after an action a, and chooses the next action.  \nExample 1 (RL Spaceship Agent) . Let’s imagine an RL agent learning to land a spaceship on the surface of a planet. The agent gets information about position, angle, angular velocity, and whether the landing legs touch the ground. It can fire the main engine, left thruster, right thruster, or do nothing. During training, the agent earns rewards for landing in the target location.  \nUnfortunately, trained policies can exhibit unsafe behavior (Gross et al. , 2022) like collisions (see Example 2), are vulnerable to attacks (Huang et al., 2017) like noised observations from a defect sensor or attacker (see Example 3), and testing RL policies for the two previous mentioned issues consumes considerable effort inhuman and machine time (Alshahwan et al., 2023; Dakhel et al., 2023; Khaliq et al., 2022) .  \nExample 2 (Unsafe Behavior) . A trained RL spaceship agent might exhibit unsafe behavior during landing, such as colliding with the surface at high speeds, since its reward function may focus too much on fast landing.  \nFigure 2: Parallel MARL setup with two agents performing a joint action a = a 1 × a2 , leading to a new states and reward rew, divided into observations o 1 and o2 .  \n RL Agent 1 π 1 (o)  a 1  \no 1 = O (s1 ) , rew 1  Environment  a2  \no2 = O (s2 ) , rew2  RL Agent 2 π2 (o)   \nFigure 3: Turn-based MARL system with two agents interacting sequentially with a shared environment. Each agent receives observations (o1 and o2 ) and rewards (rew1 and rew2 ) after their actions (a1 and a2 ) .  \nEven though the agent might have learned a policy that achieves successful landings in ideal conditions, slight deviations in environmental parameters (e.g., terrain slopes or varying gravity) could cause the agent to perform risky maneuvers, like excessive lateral movement, resulting in collisions.  \nExample 3 (Adversarial Attack) . 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