Dual-Policy Fusion for Multitask Multiagent Reinforcement Learning

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.