Difference between revisions of "Deep Q Network (DQN)"
| Line 11: | Line 11: | ||
* [[Deep Reinforcement Learning (DRL)]] | * [[Deep Reinforcement Learning (DRL)]] | ||
* [[Reinforcement Learning (RL)]] | * [[Reinforcement Learning (RL)]] | ||
| − | + | ||
| − | |||
* [[Gaming]] | * [[Gaming]] | ||
| − | |||
| − | + | Deep Q learning, 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. [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 agent, termed a deep Q-network (DQN) [http://deepmind.com/research/dqn/ Human-level control through Deep Reinforcement Learning | Deepmind] | ||
Revision as of 13:30, 1 September 2019
Youtube search... ...Google search
Deep Q learning, 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. 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
