Abstract: To address the issues of poor training performance and insufficient generalization capability of most classical imitation learning methods in few-shot scenarios due to data scarcity, a meta-learning based generative adversarial imitation learning(Meta-GAIL)method was proposed. Through the introduction of meta-learning mechanisms, the policy network pre-accumulated experiential knowledge from diverse tasks with similar characteristics to the target task. The generative adversarial imitation learning(GAIL)algorithm was utilized to fine-tune the network using the limited demonstration data provided by the target task, achieving rapid adaptive transfer to new tasks. To validate the effectiveness of the method, systematic experiments were conducted on the MuJoCo physics simulation platform, where Meta-GAIL method was compared and evaluated against baseline algorithms. Experimental results demonstrated that Meta-GAIL method exhibited stronger rapid adaptability in unseen similar task scenarios by effectively integrating cross-task knowledge representations acquired during the meta-learning phase, and its performance consistently outperformed baseline algorithms under few-shot settings.

Key words: few-shot learning, imitation learning, generative adversarial imitation learning, meta-learning, generalization