Research on Isomorphic Task Transfer Algorithm Based on Knowledge Distillation in Multi-Agent Collaborative Systems.

Area of Science:

• Artificial Intelligence
• Robotics
• Computer Science

Background:

• Multi-agent collaborative systems face challenges adapting strategies to evolving task scenarios and increasing agent numbers.
• Existing collaborative strategies often struggle with effective transfer learning to new tasks.

Purpose of the Study:

• To propose a novel knowledge distillation method combined with a domain separation network (DSN-KD) for enhanced transfer learning in multi-agent systems.
• To reduce the cost of transfer learning by avoiding complex state-action mapping pre-design and training.

Main Methods:

• Leveraging a well-performing policy network from a source task as a teacher model.
• Utilizing a domain-separated neural network structure to correct teacher model outputs for supervision.
• Guiding agent learning in new tasks through the DSN-KD framework.

Main Results:

• The DSN-KD method significantly enhances the learning speed of policies for new tasks.
• The proposed method improves the proximity of the policy model to the theoretically optimal policy in practical applications.
• Experimental validation across diverse scenarios including UAV operations and robot cooperation.

Conclusions:

• The DSN-KD transfer method offers an effective solution for adapting multi-agent systems to new task scenarios.
• This approach reduces the complexity and cost associated with traditional transfer learning methods.
• The findings demonstrate the practical utility and efficiency of DSN-KD in complex collaborative environments.