arXiv:2511.02969v2 Announce Type: replace 
Abstract: Efficient exploration in deep reinforcement learning remains a fundamental challenge, especially in environments characterized by high-dimensional states and sparse rewards. Traditional exploration strategies that rely on random local policy noise, such as $\epsilon$-greedy and Boltzmann exploration methods, often struggle to efficiently balance exploration and exploitation. In this paper, we integrate the notion of (expected) value of information (EVOI) within the well-known Bootstrapped DQN algorithmic framework, to enhance the algorithm's deep exploration ability. Specifically, we develop two novel algorithms that incorporate the expected gain from learning the value of information into Bootstrapped DQN. Our methods use value of information estimates to measure the discrepancies of opinions among distinct network heads, and drive exploration towards areas with the most potential. We evaluate our algorithms with respect to performance and their ability to exploit inherent uncertainty arising from random network initialization. Our experiments in complex, sparse-reward Atari games demonstrate increased performance, all the while making better use of uncertainty, and, importantly, without introducing extra hyperparameters.

قدمت دراسة جديدة خوارزميتين مبتكرتين تعززان إطار عمل Bootstrapped DQN من خلال دمج القيمة المتوقعة للمعلومات (EVOI) لتحسين الاستكشاف في التعلم العميق المعزز. تتناول هذه الطريقة التحديات المتعلقة بالاستكشاف الفعال في البيئات ذات الأبعاد العالية والمكافآت النادرة، والتي تكافح الأساليب التقليدية لإدارتها.

Un nuevo estudio ha introducido dos algoritmos novedosos que mejoran el marco de Bootstrapped DQN al integrar el valor esperado de la información (EVOI) para mejorar la exploración en el aprendizaje por refuerzo profundo. Este enfoque aborda los desafíos de la exploración eficiente en entornos de alta dimensión con recompensas escasas, que las metodologías tradicionales luchan por gestionar.

Une nouvelle étude a présenté deux algorithmes novateurs qui améliorent le cadre Bootstrapped DQN en intégrant la valeur d'information attendue (EVOI) pour améliorer l'exploration dans l'apprentissage par renforcement profond. Cette approche répond aux défis d'une exploration efficace dans des environnements de haute dimension avec des récompenses rares, que les méthodes traditionnelles peinent à gérer.

A new study has introduced two novel algorithms that enhance the Bootstrapped DQN framework by integrating the expected value of information (EVOI) to improve exploration in deep reinforcement learning. This approach addresses the challenges of efficient exploration in high-dimensional environments with sparse rewards, which traditional methods struggle to manage.

Value of Information-Enhanced Exploration in Bootstrapped DQN

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Value of Information-Enhanced Exploration in Bootstrapped DQN

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