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Transfer Learning in Deep Reinforcement Learning: A Survey.

Zhuangdi Zhu, Kaixiang Lin, Anil K Jain

IEEE Transactions on Pattern Analysis and Machine Intelligence

|July 4, 2023

Summary

This summary is machine-generated.

This survey explores transfer learning (TL) in deep reinforcement learning (DRL). We categorize state-of-the-art TL approaches to enhance DRL efficiency and effectiveness in complex tasks.

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Area of Science:

Artificial Intelligence
Machine Learning
Deep Learning

Background:

Reinforcement learning (RL) is a key paradigm for sequential decision-making.
Deep neural networks have significantly advanced RL capabilities.
Challenges in RL efficiency and effectiveness necessitate knowledge transfer.

Purpose of the Study:

To systematically investigate recent progress in transfer learning for deep reinforcement learning.
To provide a framework for categorizing state-of-the-art transfer learning approaches.
To analyze goals, methodologies, backbones, and applications of transfer learning in DRL.

Main Methods:

Literature review and systematic categorization of transfer learning techniques.
Analysis of transfer learning approaches based on defined criteria.
Exploration of connections to related RL topics and future challenges.

Main Results:

A comprehensive framework for understanding transfer learning in DRL.
Categorization of various transfer learning methods and their applications.
Identification of current limitations and future research directions.

Conclusions:

Transfer learning is crucial for advancing deep reinforcement learning.
Further research is needed to address challenges and unlock full potential.
This survey provides a valuable resource for researchers and practitioners in DRL.