Playing Atari Breakout Using Deep Reinforcement Learning

Sammanfattning

This report investigates the implementation of a Deep Reinforcement Learning (DRL) algorithm for complex tasks. The complex task chosen was the classic game Breakout, first introduced on the Atari 2600 console.The selected DRL algorithm was Deep Q-Network(DQN) since it is one of the first and most fundamental DRL algorithms. To test the DQN algorithm, it was first applied to CartPole which is a common control theory problem, using values describing the system as input.The implementation was then slightly modified to process images when employed for Breakout, in which it was successful. The application received a higher score than a professional human game tester. However, work remains to be done to achieve performance similar to state-of-theartimplementations of the DQN algorithm.