Difference between revisions of "Deep Q Network (DQN)"

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|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools  
 
|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools  
 
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[http://www.youtube.com/results?search_query=deep+reinforcement+q+learning+artificial+intelligence+ Youtube search...]
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[https://www.youtube.com/results?search_query=deep+reinforcement+q+learning+artificial+intelligence+ Youtube search...]
[http://www.google.com/search?q=deep+reinforcement+q+learning+machine+learning+ML+artificial+intelligence ...Google search]
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[https://www.google.com/search?q=deep+reinforcement+q+learning+machine+learning+ML+artificial+intelligence ...Google search]
  
 
* [[Reinforcement Learning (RL)]]
 
* [[Reinforcement Learning (RL)]]
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* [[Gaming]]
  
Deep Q learning (DQN), as published in [http://arxiv.org/abs/1312.5602 Playing Atari with Deep Reinforcement Learning | Mnih et al, 2013], leverages advances in deep learning to learn policies from high dimensional sensory input. A convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards. [http://towardsdatascience.com/dqn-part-1-vanilla-deep-q-networks-6eb4a00febfb Vanilla Deep Q Networks: Deep Q Learning Explained | Chris Yoon - Towards Data Science]
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Deep Q learning (DQN), as published in [https://arxiv.org/abs/1312.5602 Playing Atari with Deep Reinforcement Learning | Mnih et al, 2013], leverages advances in deep learning to learn policies from high dimensional sensory input. A convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards. [https://towardsdatascience.com/dqn-part-1-vanilla-deep-q-networks-6eb4a00febfb Vanilla Deep Q Networks: Deep Q Learning Explained | Chris Yoon - Towards Data Science]
  
Training deep neural networks to show that a novel end-to-end reinforcement learning [[Agents|agent]], termed a deep Q-network (DQN) [http://deepmind.com/research/dqn/ Human-level control through Deep Reinforcement Learning | Deepmind]
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Training deep neural networks to show that a novel end-to-end reinforcement learning [[Agents|agent]], termed a deep Q-network (DQN) [https://deepmind.com/research/dqn/ Human-level control through Deep Reinforcement Learning | Deepmind]
  
 
<youtube>79pmNdyxEGo</youtube>
 
<youtube>79pmNdyxEGo</youtube>

Latest revision as of 08:09, 28 March 2023

Youtube search... ...Google search


Deep Q learning (DQN), as published in Playing Atari with Deep Reinforcement Learning | Mnih et al, 2013, leverages advances in deep learning to learn policies from high dimensional sensory input. A convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards. Vanilla Deep Q Networks: Deep Q Learning Explained | Chris Yoon - Towards Data Science

Training deep neural networks to show that a novel end-to-end reinforcement learning agent, termed a deep Q-network (DQN) Human-level control through Deep Reinforcement Learning | Deepmind