Knáb, István Gellért and Bécsi, Tamás and Kővári, Bálint (2025) Guided Experience Prioritization for Continuous Reinforcement Learning. IEEE ACCESS, 13. pp. 180145-180155. ISSN 2169-3536 10.1109/ACCESS.2025.3622494

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Abstract

A primary limiting factor in modern deep learning is the availability of computational resources, a constraint that becomes particularly pronounced in the context of complex reinforcement learning tasks. Hardware performance limitations often result in prolonged training procedures, which are frequently correlated with increased operational costs. In many cases, these limitations place such solutions at a disadvantage, which is why cost reduction remains one of the main priorities. In the field of deep reinforcement learning, a commonly employed method for this purpose is the guided selection of training samples, which emphasizes the most informative state transitions, thereby allowing the agent to maximize its learning from the most critical experiences. However, similarly to the decision-making process, it is also crucial to ensure that the agent has information about which samples are important. Therefore, exploration must be incorporated in this context as well, enabling the agent to acquire the most diverse possible knowledge regarding both the samples themselves and their informational value. The paper introduces a sampling method for continuous-output reinforcement learning agents, which balances, in a problem-independent manner, the necessary exploration and the selection of the most informative samples that enhance learning speed. Due to the presented Upper Confidence Bound-based sampling, both exploration and exploitation are ensured simultaneously, and by tuning their balance appropriately, training can be accelerated. This approach improves the efficiency of the training process, which often constitutes the primary bottleneck.

Item Type:	Article
Subjects:	Q Science > QA Mathematics and Computer Science > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Divisions:	Systems and Control Lab
SWORD Depositor:	MTMT Injector
Depositing User:	MTMT Injector
Date Deposited:	20 Jan 2026 19:50
Last Modified:	20 Jan 2026 19:50
URI:	https://eprints.sztaki.hu/id/eprint/11073

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