Difference between revisions of "Deep Q Network (DQN)"
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| − | == | + | {{#seo: |
| − | [ | + | |title=PRIMO.ai |
| + | |titlemode=append | ||
| + | |keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS | ||
| + | |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | ||
| + | }} | ||
| + | [https://www.youtube.com/results?search_query=deep+reinforcement+q+learning+artificial+intelligence+ Youtube search...] | ||
| + | [https://www.google.com/search?q=deep+reinforcement+q+learning+machine+learning+ML+artificial+intelligence ...Google search] | ||
| + | |||
| + | * [[Reinforcement Learning (RL)]] | ||
| + | ** [[Monte Carlo]] (MC) Method - Model Free Reinforcement Learning | ||
| + | ** [[Markov Decision Process (MDP)]] | ||
| + | ** [[State-Action-Reward-State-Action (SARSA)]] | ||
| + | ** [[Q Learning]] | ||
| + | *** Deep Q Network (DQN) | ||
| + | ** [[Deep Reinforcement Learning (DRL)]] DeepRL | ||
| + | ** [[Distributed Deep Reinforcement Learning (DDRL)]] | ||
| + | ** [[Evolutionary Computation / Genetic Algorithms]] | ||
| + | ** [[Actor Critic]] | ||
| + | *** [[Asynchronous Advantage Actor Critic (A3C)]] | ||
| + | *** [[Advanced Actor Critic (A2C)]] | ||
| + | *** [[Lifelong Latent Actor-Critic (LILAC)]] | ||
| + | ** [[Hierarchical Reinforcement Learning (HRL)]] | ||
| + | |||
| − | |||
* [[Gaming]] | * [[Gaming]] | ||
| − | |||
| − | + | 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 agent, termed a deep Q-network (DQN) [ | + | 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> | ||
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<youtube>V1eYniJ0Rnk</youtube> | <youtube>V1eYniJ0Rnk</youtube> | ||
| − | <youtube> | + | <youtube>fevMOp5TDQs</youtube> |
| + | <youtube>5fHngyN8Qhw</youtube> | ||
Latest revision as of 09:09, 28 March 2023
Youtube search... ...Google search
- Reinforcement Learning (RL)
- Monte Carlo (MC) Method - Model Free Reinforcement Learning
- Markov Decision Process (MDP)
- State-Action-Reward-State-Action (SARSA)
- Q Learning
- Deep Q Network (DQN)
- Deep Reinforcement Learning (DRL) DeepRL
- Distributed Deep Reinforcement Learning (DDRL)
- Evolutionary Computation / Genetic Algorithms
- Actor Critic
- Hierarchical Reinforcement Learning (HRL)
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
