Domain Adaptation for Imitation Learning Using Generative Adversarial Network.

Area of Science:

• Robotics
• Machine Learning
• Artificial Intelligence

Background:

• Imitation learning enables agents to acquire control policies from expert demonstrations when reward functions are absent.
• Standard imitation learning assumes identical domain configurations between agents and expert demonstrations, limiting policy transferability to distinct domains.
• Domain adaptive imitation learning addresses the challenge of applying learned policies in new environments using expert data from a different domain.

Purpose of the Study:

• To develop a novel approach for domain adaptive imitation learning.
• To enable autonomous agents to learn optimal policies in a target domain using demonstrations from a distinct source domain.
• To overcome the limitations of standard imitation learning methods in cross-domain policy transfer.

Main Methods:

• A Generative Adversarial Network (GAN)-based model is proposed to address domain adaptive imitation learning.
• The model is designed to learn both domain-shared and domain-specific features from expert demonstrations.
• These learned features are utilized to derive an optimal control policy that generalizes across domains.

Main Results:

• The proposed GAN-based model demonstrated effectiveness in domain adaptive imitation learning.
• The model successfully learned to transfer policies across different domains.
• Experimental results validated the model's performance on tasks with varying complexity, from low to high-dimensional.

Conclusions:

• The proposed Generative Adversarial Network model provides an effective solution for domain adaptive imitation learning.
• This approach enhances the applicability of learned policies by enabling cross-domain transfer.
• The method shows promise for real-world applications requiring robust policy learning in diverse environments.