{"type":"ScholarlyArticle","title":"Continual Multi-Objective Reinforcement Learning via Reward Model Rehearsal","authors":[{"name":"Lihe Li","slug":"lihe-li","given":"Lihe","family":"Li","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University; School of Artificial Intelligence , Nanjing University"},{"name":"Ruotong Chen","slug":"ruotong-chen","given":"Ruotong","family":"Chen","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University; School of Artificial Intelligence , Nanjing University"},{"name":"Ziqian Zhang","slug":"ziqian-zhang","given":"Ziqian","family":"Zhang","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University; School of Artificial Intelligence , Nanjing University"},{"name":"Zhichao Wu","slug":"zhichao-wu","given":"Zhichao","family":"Wu","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University; School of Artificial Intelligence , Nanjing University"},{"name":"Yi-Chen Li","slug":"yi-chen-li","given":"Yi-Chen","family":"Li","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University; School of Artificial Intelligence , Nanjing University; Polixir Technologies"},{"name":"Cong Guan","slug":"cong-guan","given":"Cong","family":"Guan","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University; School of Artificial Intelligence , Nanjing University"},{"name":"Yang Yu","slug":"yang-yu","given":"Yang","family":"Yu","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University; School of Artificial Intelligence , Nanjing University; Polixir Technologies"},{"name":"Lei Yuan","slug":"lei-yuan","given":"Lei","family":"Yuan","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University; School of Artificial Intelligence , Nanjing University; Polixir Technologies"}],"abstract":"Multi-objective reinforcement learning (MORL) approaches address real-world problems with multiple objectives by learning policies maximizing returns weighted by different user preferences. Typical methods assume the objectives remain unchanged throughout the agent's lifetime. However, in some real-world situations, the agent may encounter dynamically changing learning objectives, i.e., different vector-valued reward functions at different learning stages. This issue has not been considered in problem formulation or algorithm design. To address this issue, we formalize the setting as a continual MORL (CMORL) problem for the first time, accounting for the evolution of objectives throughout the learning process. Subsequently, we propose Continual Multi-Objective Reinforcement Learning via Reward Model Rehearsal (CORe3), incorporating a dynamic agent network for rapid adaptation to new objectives. Moreover, we develop a reward model rehearsal technique to recover the reward signals for previous objectives, thus alleviating catastrophic forgetting. Experiments on four CMORL benchmarks showcase that CORe3 effectively learns policies satisfying different preferences on all encountered objectives, and outperforms the best baseline by 171%, highlighting the capability of CORe3 to handle situations with evolving objectives.","doi":"10.24963/ijcai.2024/490","url":"https://ijcai.org/proceedings/2024/490","pdf":"https://ijcai.org/proceedings/2024/0490.pdf","markdown":"https://ijcai.org/proceedings/2024/490.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/490","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":490,"pages":{"first":4434,"last":4442},"keywords":["Machine Learning: ML: Incremental learning","Machine Learning: ML: Reinforcement learning","Machine Learning: ML: Optimization"],"videos":[],"references":[{"position":1,"raw":"[Abels et al., 2019] Axel Abels, Diederik M. 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