Deep Q Network (DQN) - Revision history

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2023-03-28T13:09:03Z

Text replacement - "http:" to "https:"

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BPeat at 21:49, 1 September 2019

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@@ Line 5: / Line 5: @@
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
-[http://www.youtube.com/results?search_query=deep+reinforcement+q+learning+artificial+intelligence+ Youtube search...]
+[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]
+[https://www.google.com/search?q=deep+reinforcement+q+learning+machine+learning+ML+artificial+intelligence ...Google search]
 * [[Reinforcement Learning (RL)]]
@@ Line 26: / Line 26: @@
 * [[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]
+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]
+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>

@@ Line 28: / Line 28: @@
 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]
-Training deep neural networks to show that a novel end-to-end reinforcement learning agent, termed a deep Q-network (DQN) [http://deepmind.com/research/dqn/ Human-level control through Deep Reinforcement Learning | Deepmind]
+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]
 <youtube>79pmNdyxEGo</youtube>

@@ Line 18: / Line 18: @@
 ** [[Evolutionary Computation / Genetic Algorithms]]
 ** [[Actor Critic]]
 *** [[Advanced Actor Critic (A2C)]]
 *** [[Lifelong Latent Actor-Critic (LILAC)]]
 ** [[Hierarchical Reinforcement Learning (HRL)]]
 * [[Gaming]]

@@ Line 8: / Line 8: @@
 [http://www.google.com/search?q=deep+reinforcement+q+learning+machine+learning+ML+artificial+intelligence ...Google search]
-* Reinforcement Learning (RL):
+* [[Reinforcement Learning (RL)]]
 ** [[Monte Carlo]] (MC) Method - Model Free Reinforcement Learning
 ** [[Markov Decision Process (MDP)]]
 ** [[Q Learning]]
-** [[State-Action-Reward-State-Action (SARSA)]]
+*** Deep Q Network (DQN)
 ** [[Deep Reinforcement Learning (DRL)]] DeepRL
 ** [[Distributed Deep Reinforcement Learning (DDRL)]]
 ** [[Evolutionary Computation / Genetic Algorithms]]
 ** [[Actor Critic]]
 ** [[Hierarchical Reinforcement Learning (HRL)]]
 * [[Gaming]]

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 <youtube>79pmNdyxEGo</youtube>
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