Chrome Dino Run using Reinforcement Learning

• URL: http://arxiv.org/abs/2008.06799v1
• Date: Sat, 15 Aug 2020 22:18:20 GMT
• Title: Chrome Dino Run using Reinforcement Learning
• Authors: Divyanshu Marwah, Sneha Srivastava, Anusha Gupta, Shruti Verma
• Abstract summary: We study most popular model free reinforcement learning algorithms along with convolutional neural network to train the agent for playing the game of Chrome Dino Run. We have used two of the popular temporal difference approaches namely Deep Q-Learning, and Expected SARSA and also implemented Double DQN model to train the agent.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reinforcement Learning is one of the most advanced set of algorithms known to mankind which can compete in games and perform at par or even better than humans. In this paper we study most popular model free reinforcement learning algorithms along with convolutional neural network to train the agent for playing the game of Chrome Dino Run. We have used two of the popular temporal difference approaches namely Deep Q-Learning, and Expected SARSA and also implemented Double DQN model to train the agent and finally compare the scores with respect to the episodes and convergence of algorithms with respect to timesteps.

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