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Efficient Multitask Reinforcement Learning Without Performance Loss.

Jongchan Baek, Seungmin Baek, Soohee Han

IEEE Transactions on Neural Networks and Learning Systems

|June 7, 2023

Summary

This study introduces an iterative sparse Bayesian policy optimization (ISBPO) method for efficient multitask reinforcement learning (RL) in industrial control. ISBPO preserves prior knowledge, enhances resource use, and boosts sample efficiency for continual learning tasks.

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Area of Science:

Artificial Intelligence
Machine Learning
Control Systems Engineering

Background:

Industrial control systems require high performance and cost-effective solutions.
Continual learning in reinforcement learning (RL) presents challenges in preserving past knowledge while acquiring new skills.

Purpose of the Study:

To develop an efficient multitask reinforcement learning (RL) method for industrial control.
To propose an iterative sparse Bayesian policy optimization (ISBPO) scheme for continual learning scenarios.

Main Methods:

Introduced an iterative sparse Bayesian policy optimization (ISBPO) scheme.
Employed an iterative pruning method to preserve performance of previously learned tasks.
Utilized sparse Bayesian policy optimization (SBPO) for pruning-aware policy optimization.

Main Results:

ISBPO enables sequential learning of multiple tasks within a single policy neural network.
The method completely preserves control performance of previously learned tasks.
ISBPO improves sample efficiency and performance for learning new tasks through weight sharing and reuse.

Conclusions:

The proposed ISBPO scheme is highly suitable for sequentially learning multiple tasks in industrial control.
ISBPO demonstrates effectiveness in performance conservation, efficient resource utilization, and enhanced sample efficiency.
The method offers a robust solution for continual learning in complex control applications.