Interactive Reinforcement Learning Speedup with Confidence-based Transfer Learning

Reinforcement learning (RL) is a type of machine learning that focuses on allowing a physical or virtual agent to complete sequential decision-making tasks, such as video games. It has had many successes, but can be slow in practice, requiring large amounts of data. This project aims to speed up such learning problems by leveraging information from an existing agent. This existing agent need not be perfect the algorithm developed will leverage information from the existing agent whenever possible and learn to outperform it where it is suboptimal.

Faculty Supervisor:

Martha White

Student:

Zhaodong Wang

Partner:

Borealis AI

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

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