{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T16:22:02Z","timestamp":1759335722920},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7]]},"abstract":"<jats:p>Deep reinforcement learning (DRL) has been studied in a variety of challenging decision-making tasks, e.g., autonomous driving. \\textcolor{black}{However, DRL typically suffers from the action shaking problem, which means that agents can select actions with big difference even though states only slightly differ.} One of the crucial reasons for this issue is the inappropriate design of the reward in DRL. In this paper, to address this issue, we propose a novel way to incorporate the smoothness of actions in the reward. Specifically, we introduce sub-rewards and add multiple constraints related to these sub-rewards. In addition, we propose a multi-constraint proximal policy optimization (MCPPO) method to solve the multi-constraint DRL problem. Extensive simulation results show that the proposed MCPPO method has better action smoothness compared with the traditional proportional-integral-differential (PID) and mainstream DRL algorithms. The video is available at https:\/\/youtu.be\/F2jpaSm7YOg.<\/jats:p>","DOI":"10.24963\/ijcai.2022\/528","type":"proceedings-article","created":{"date-parts":[[2022,7,16]],"date-time":"2022-07-16T02:55:56Z","timestamp":1657940156000},"page":"3802-3808","source":"Crossref","is-referenced-by-count":1,"title":["Multi-Constraint Deep Reinforcement Learning for Smooth Action Control"],"prefix":"10.24963","author":[{"given":"Guangyuan","family":"Zou","sequence":"first","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University"},{"name":"Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)"}]},{"given":"Ying","family":"He","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University"},{"name":"Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)"}]},{"given":"F. Richard","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University"},{"name":"Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)"}]},{"given":"Longquan","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University"},{"name":"Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)"}]},{"given":"Weike","family":"Pan","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University"}]},{"given":"Zhong","family":"Ming","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University"}]}],"member":"10584","event":{"number":"31","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2022","name":"Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}","start":{"date-parts":[[2022,7,23]]},"theme":"Artificial Intelligence","location":"Vienna, Austria","end":{"date-parts":[[2022,7,29]]}},"container-title":["Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T11:10:12Z","timestamp":1658142612000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2022\/528"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2022\/528","relation":{},"subject":[],"published":{"date-parts":[[2022,7]]}}}