{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:36:22Z","timestamp":1761176182895,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Efficient exploration in high-dimensional observation spaces remains a critical challenge in deep reinforcement learning, particularly in scenarios with sparse extrinsic rewards. A promising approach is to encourage exploration by estimating intrinsic rewards based on the novelty of observations. However, there is a gap between the observed novelty and the actual effectiveness of exploration, as both environmental stochasticity and the agent\u2019s actions may influence observations. To accurately evaluate the novelty contributed by agent exploration in intrinsic rewards, we propose the AIRES (Attention-driven Intrinsic Reward for Exploration Strategy) framework. AIRES leverages the attention mechanisms to analyze the relationship within trajectory sequences generated by agent-environment interactions, employing attention weights to quantify the relevance of observations to actions. By applying attention weights to intrinsic rewards, the novelty brought by agent exploration is enhanced and the impact of environmental stochasticity is reduced. Extensive experiments demonstrate that AIRES significantly enhances the performance of prominent intrinsic reward methods, establishing it as a robust and scalable solution for efficient exploration.<\/jats:p>","DOI":"10.3233\/faia251028","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:49:20Z","timestamp":1761126560000},"source":"Crossref","is-referenced-by-count":0,"title":["AIRES: A General Framework for Efficient Intrinsic Rewards Based on Attention Mechanisms"],"prefix":"10.3233","author":[{"given":"Xin","family":"Liu","sequence":"first","affiliation":[{"name":"Intelligent Game and Decision Lab (IGDL), Beijing, China"}]},{"given":"Jie","family":"Tan","sequence":"additional","affiliation":[{"name":"Intelligent Game and Decision Lab (IGDL), Beijing, China"}]},{"given":"Li","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University, China"}]},{"given":"Xu","family":"Wang","sequence":"additional","affiliation":[{"name":"Intelligent Game and Decision Lab (IGDL), Beijing, China"}]},{"given":"Guoli","family":"Wu","sequence":"additional","affiliation":[{"name":"Intelligent Game and Decision Lab (IGDL), Beijing, China"}]},{"given":"Xiaoguang","family":"Ren","sequence":"additional","affiliation":[{"name":"Intelligent Game and Decision Lab (IGDL), Beijing, China"}]},{"given":"Huadong","family":"Dai","sequence":"additional","affiliation":[{"name":"Intelligent Game and Decision Lab (IGDL), Beijing, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251028","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:49:21Z","timestamp":1761126561000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251028"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251028","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}