Comparison of Deep Reinforcement Learning Approaches for Intelligent Game Playing
In Reinforcement Learning, a category of machine learning, learning is based on evaluative feedbacks without any supervised signals. The paper presents work aimed to understand the deep reinforcement learning approaches to creating such intelligent agents, by reproducing existing research and comparing their results. The project uses the Atari 2600 game called Breakout, in which the agent will learn control policies using deep reinforcement learning approaches to achieve a high score. The project explores two deep reinforcement learning approaches, Asynchronous Advantage actor critic and Deep Q Learning, both proposed by the DeepMind team, to train intelligent agents that can interact with an environment with automatic feature engineering thus requiring minimal domain knowledge.
2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC)