提出FM-EAC框架，提升动态环境多任务控制的泛化能力。
FM-EAC: Feature Model-based Enhanced Actor-Critic for Multi-Task Control in Dynamic Environments
- 基于特征建模融合规划与决策，结合模型强化学习与无模型方法优势。
- 在城市和农业场景仿真中，性能超越多个先进MBRL与MFRL方法。
- 支持按需定制子网络，适用于不同应用场景的灵活部署。
基于模型的强化学习（MBRL）与无模型强化学习（MFRL）虽路径不同，但在Dyna-Q设计中趋于融合。然而，当前强化学习方法在跨任务与场景间的迁移能力仍有限。针对此问题，本文提出通用算法特征模型增强型演员-评论家（FM-EAC），集成规划、执行与学习，用于动态环境中的多任务控制。该方法通过新型特征基模型与增强型演员-评论家框架，结合MBRL与MFRL优势，显著提升泛化能力。在城市与农业应用的仿真测试中，FM-EAC持续优于多种先进MBRL与MFRL方法。更重要的是，可根据用户需求定制不同子网络，实现灵活适配。
原文摘要 · Abstract (English)
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.
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