Chrome Dino Run using Reinforcement Learning
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...
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creator | Marwah, Divyanshu Srivastava, Sneha Gupta, Anusha Verma, Shruti |
description | 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. |
doi_str_mv | 10.48550/arxiv.2008.06799 |
format | Article |
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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
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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
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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.</abstract><doi>10.48550/arxiv.2008.06799</doi><oa>free_for_read</oa></addata></record> |
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subjects | Computer Science - Artificial Intelligence Computer Science - Learning |
title | Chrome Dino Run using Reinforcement Learning |
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