Variational Information Bottleneck Regularized Deep Reinforcement Learning for Efficient Robotic Skill Adaptation.

Area of Science:

• Robotics
• Artificial Intelligence
• Machine Learning

Background:

• Deep Reinforcement Learning (DRL) shows promise for robotic skill learning but struggles with real-world deployment due to environmental gaps.
• Dynamic environments exacerbate the challenges of transferring DRL skills from training to deployment settings.

Purpose of the Study:

• To develop an efficient meta-reinforcement learning algorithm for robotic skill transfer in dynamic environments.
• To address the training-deployment environment gap in safety-critical robotic systems.

Main Methods:

• A meta-reinforcement learning algorithm integrating a variational information bottleneck (VIB) and maximum entropy regularization.
• VIB is used during meta-training to infer basic tasks, enabling learning of consistent basic skills.
• Skills for new tasks are generated by combining learned basic skills.

Main Results:

• The proposed algorithm improved sample efficiency by 200-5000 times on new robotic locomotion tasks.
• Significant asymptotic performance improvements were observed.
• The framework demonstrates enhanced robotic skill transferring capabilities in dynamic environments.

Conclusions:

• The variational information bottleneck regularized deep reinforcement learning framework offers a significant advancement for deploying DRL in practical robotic systems.
• The approach facilitates efficient and robust robotic skill transfer, even in challenging, ever-changing environments.