Generative Upper-Level Policy Imitation Learning With Pareto-Improvement for Energy-Efficient Advanced Machining...

Area of Science:

• Manufacturing Engineering
• Artificial Intelligence
• Machine Learning

Background:

• Advanced Machining Systems (AMSs) possess intelligence for process improvement.
• Imitation Learning (IL) can leverage this intelligence by observing expert demonstrations.
• Existing IL methods struggle with heterogeneous expert data and optimizing for 'green' objectives.

Purpose of the Study:

• To develop a novel three-phase policy search algorithm based on IL.
• To enable learning from heterogeneous expert policies in AMS tasks.
• To balance machining performance with energy conservation objectives.

Main Methods:

• A three-phase policy search algorithm integrating IL, Pareto-improvement learning, and ensemble policies.
• Phase 1: Upper-level policy learning for machining basics and diverse decision-making.
• Phase 2: Pareto-improvement learning for energy conservation on a policy manifold.
• Phase 3: Ensemble policies and human feedback amplification.

Main Results:

• The proposed IL method successfully learns heterogeneous expert policies.
• The algorithm effectively balances machining performance with energy efficiency.
• Experimental results show superior solution quality and faster computation times than meta-heuristics and baseline methods.

Conclusions:

• The novel IL approach enhances AMS capabilities by learning from diverse experts and optimizing for energy efficiency.
• This method addresses limitations of current IL by handling varied expert data and incorporating green manufacturing objectives.
• The proposed algorithm offers a significant advancement in intelligent machining systems.