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The Use of Reinforcement Learning in Gaming The Breakout Game Case Study
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  • Ao Chen ,
  • Taresh Dewan ,
  • Manva Trivedi ,
  • Danning Jiang ,
  • Aloukik Aditya ,
  • Sabah Mohammed
Ao Chen
Lakehead University

Corresponding Author:[email protected]

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Taresh Dewan
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Manva Trivedi
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Danning Jiang
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Aloukik Aditya
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Sabah Mohammed
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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.