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The Use of Reinforcement Learning in Gaming The Breakout Game Case Study.pdf (647.28 kB)

The Use of Reinforcement Learning in Gaming The Breakout Game Case Study.pdf

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posted on 04.04.2020, 15:28 by Ao Chen, Taresh Dewan, Manva Trivedi, Danning Jiang, Aloukik Aditya, Sabah Mohammed
This paper provides a comparative analysis between Deep Q Network (DQN) and Double Deep Q Network (DDQN) algorithms based on their hit rate, out of which DDQN proved to be better for Breakout game. DQN is chosen over Basic Q learning because it understands policy learning using its neural network which is good for complex environment and DDQN is chosen as it solves overestimation problem (agent always choses non-optimal action for any state just because it has maximum Q-value) occurring in basic Q-learning.

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Email Address of Submitting Author

achen11@lakeheau.ca

Submitting Author's Institution

Lakehead University

Submitting Author's Country

Canada

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