A quantum enhanced learning agent for basic games: a case study

Abstract

In this thesis, we investigate the speedup we can achieve with quantum enhanced reinforcement learning compared to classical reinforcement learning. Reinforcement learning is a classical main model in machine learning and is based on the concept of trial and error, as well as receiving rewards for a correct behavior to learn speciffic tasks.

In this thesis a quantum enhanced agent is implemented to play the game of tic-tac-toe. The example of tic-tac-toe is used to analyze the learning performance of the agent. The main goal of this thesis is to speed-up the learning time of the quantum enhanced agent in this setting, which means to reduce the number of games it has to play until it has learned. Within the process, the learning time of the quantum enhanced agent is compared to a completely classical agent. The comparison between those types of agents is made trough a simulation on a classical computer.