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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.15430 |
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| _version_ | 1866914205529014272 |
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| author | Zhou, Quanxi Mao, Wencan Tsukada, Manabu Lui, John C. S. Ji, Yusheng |
| author_facet | Zhou, Quanxi Mao, Wencan Tsukada, Manabu Lui, John C. S. Ji, Yusheng |
| contents | Model-based reinforcement learning (MBRL) and model-free reinforcement learning (MFRL) evolve along distinct paths but converge in the design of Dyna-Q [1]. However, modern RL methods still struggle with effective transferability across tasks and scenarios. Motivated by this limitation, we propose a generalized algorithm, Feature Model-Based Enhanced Actor-Critic (FM-EAC), that integrates planning, acting, and learning for multi-task control in dynamic environments. FM-EAC combines the strengths of MBRL and MFRL and improves generalizability through the use of novel feature-based models and an enhanced actor-critic framework. Simulations in both urban and agricultural applications demonstrate that FM-EAC consistently outperforms many state-of-the-art MBRL and MFRL methods. More importantly, different sub-networks can be customized within FM-EAC according to user-specific requirements. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_15430 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FM-EAC: Feature Model-based Enhanced Actor-Critic for Multi-Task Control in Dynamic Environments Zhou, Quanxi Mao, Wencan Tsukada, Manabu Lui, John C. S. Ji, Yusheng Machine Learning Artificial Intelligence Model-based reinforcement learning (MBRL) and model-free reinforcement learning (MFRL) evolve along distinct paths but converge in the design of Dyna-Q [1]. However, modern RL methods still struggle with effective transferability across tasks and scenarios. Motivated by this limitation, we propose a generalized algorithm, Feature Model-Based Enhanced Actor-Critic (FM-EAC), that integrates planning, acting, and learning for multi-task control in dynamic environments. FM-EAC combines the strengths of MBRL and MFRL and improves generalizability through the use of novel feature-based models and an enhanced actor-critic framework. Simulations in both urban and agricultural applications demonstrate that FM-EAC consistently outperforms many state-of-the-art MBRL and MFRL methods. More importantly, different sub-networks can be customized within FM-EAC according to user-specific requirements. |
| title | FM-EAC: Feature Model-based Enhanced Actor-Critic for Multi-Task Control in Dynamic Environments |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2512.15430 |