arXiv:2511.07904v3 Announce Type: replace 
Abstract: Reinforcement learning (RL) has been recognized as a powerful tool for robot control tasks. RL typically employs reward functions to define task objectives and guide agent learning. However, since the reward function serves the dual purpose of defining the optimal goal and guiding learning, it is challenging to design the reward function manually, which often results in a suboptimal task representation. To tackle the reward design challenge in RL, inspired by the satisficing theory, we propose a Test-driven Reinforcement Learning (TdRL) framework. In the TdRL framework, multiple test functions are used to represent the task objective rather than a single reward function. Test functions can be categorized as pass-fail tests and indicative tests, each dedicated to defining the optimal objective and guiding the learning process, respectively, thereby making defining tasks easier. Building upon such a task definition, we first prove that if a trajectory return function assigns higher returns to trajectories closer to the optimal trajectory set, maximum entropy policy optimization based on this return function will yield a policy that is closer to the optimal policy set. Then, we introduce a lexicographic heuristic approach to compare the relative distance relationship between trajectories and the optimal trajectory set for learning the trajectory return function. Furthermore, we develop an algorithm implementation of TdRL. Experimental results on the DeepMind Control Suite benchmark demonstrate that TdRL matches or outperforms handcrafted reward methods in policy training, with greater design simplicity and inherent support for multi-objective optimization. We argue that TdRL offers a novel perspective for representing task objectives, which could be helpful in addressing the reward design challenges in RL applications.

تم اقتراح إطار عمل جديد يسمى التعلم المعزز القائم على الاختبارات (TdRL) لتحسين مهام التحكم في الروبوتات من خلال استخدام وظائف اختبار متعددة بدلاً من وظيفة مكافأة واحدة. تهدف هذه الطريقة إلى تبسيط تعريف المهام وتحسين نتائج التعلم في تطبيقات التعلم المعزز (RL).

Se ha propuesto un nuevo marco llamado Aprendizaje por Refuerzo Basado en Pruebas (TdRL) para mejorar las tareas de control de robots utilizando múltiples funciones de prueba en lugar de una única función de recompensa. Este enfoque busca simplificar la definición de tareas y mejorar los resultados de aprendizaje en aplicaciones de aprendizaje por refuerzo (RL).

Un nouveau cadre appelé Apprentissage par Renforcement Basé sur des Tests (TdRL) a été proposé pour améliorer les tâches de contrôle des robots en utilisant plusieurs fonctions de test au lieu d'une seule fonction de récompense. Cette approche vise à simplifier la définition des tâches et à améliorer les résultats d'apprentissage dans les applications d'apprentissage par renforcement (RL).

A new framework called Test-driven Reinforcement Learning (TdRL) has been proposed to enhance robot control tasks by utilizing multiple test functions instead of a single reward function. This approach aims to simplify task definition and improve learning outcomes in reinforcement learning (RL) applications.

Test-driven Reinforcement Learning in Continuous Control

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Test-driven Reinforcement Learning in Continuous Control

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