Publication Date

Fall 2022

Degree Type

Master's Project

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Genya Ishigaki

Second Advisor

Fabio Di Troia

Third Advisor

Guangliang Chen

Keywords

reinforcement learning, tree approximation

Abstract

The goal of reinforcement learning is to learn a policy that maximizes a reward function. In some environments with complete information, search algorithms are highly useful in simulating action sequences in a game tree. However, in many practical environments, such effective search strategies are not applicable since their state transition information may not be available. This paper proposes a novel method to approximate a game tree that enables reinforcement learning to use search strategies even in incomplete information environments. With an approximated game tree, the agent predicts all possible states multiple steps into the future and evaluates the states to determine the best action sequences with the highest return. Our proposal differs from deep reinforcement learning in that it uses deep learning for not only the state evaluation but also game tree approximation. This allows it to perform better in completing complex tasks as well as learning in sparse reward environments.

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