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FM-EAC: Feature Model-based Enhanced Actor-Critic for Multi-Task Control in Dynamic Environments

arXiv 2512.15430 ↗First posted 17 Dec 2025cs.LG cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 35 references that are in the map. First posted on arXiv 17 Dec 2025.

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References · 16

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
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  3. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
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  10. 2021COMBO: Conservative Offline Model-Based Policy Optimizationcited by 365
  11. 2018Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Spacecited by 54
  12. 2019Hybrid Actor-Critic Reinforcement Learning in Parameterized Action Spacecited by 53
4 more
  1. 2019Discrete and Continuous Action Representation for Practical RL in Video Gamescited by 34
  2. 2021Sample-Efficient Reinforcement Learning via Conservative Model-Based Actor-Criticcited by 23
  3. 2019Mobile Edge Computing in Unmanned Aerial Vehicle Networkscited by 5
  4. 2023A Deep Recurrent-Reinforcement Learning Method for Intelligent AutoScaling of Serverless Functionscited by 5

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