Dual-Policy Fusion for Multitask Multiagent Reinforcement Learning
提出双策略融合方法，结合共享策略与任务特定策略，解决多任务多智能体强化学习中的负迁移问题，实验表明在同类和异类任务上均优于现有方法。
Multiagent reinforcement learning (MARL) has shown strong performance in cooperative tasks. However, most existing approaches are designed for single-task scenarios and struggle to adapt to complex and dynamic environments. Multitask MARL methods aim to improve adaptability by sharing policies across tasks, but they often suffer from negative transfer due to conflicting task-specific knowledge. To address this, we propose dual-policy fusion for multitask MARL (DPF-MTMARL), which explicitly integrates a shared policy for leveraging common knowledge and task-specific policies for capturing task-specific information. Specifically, in DPF-MTMARL, we propose a learning method to efficiently train the task-specific policies and provide corresponding theoretical analysis. Additionally, we derive the theoretical conditions for decentralizing the joint policy and enforce these conditions through a regularization term during training. Extensive experiments demonstrate that DPF-MTMARL significantly outperforms state-of-the-art baselines in both homogeneous and heterogeneous task sets, effectively mitigating negative transfer and enabling robust multitask learning.