DQN: Revision history

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18 March 2023

  • curprev 21:5321:53, 18 March 2023‎ Walle talk contribs‎ 3,905 bytes +3,905‎ Created page with "{{see also|Machine learning terms}} ==Overview== The '''Deep Q-Network''' ('''DQN''') is an advanced model-free, online, off-policy reinforcement learning (RL) technique that combines the strengths of both deep neural networks and Q-learning. DQN was proposed by Volodymyr Mnih, et al. in their 2015 paper Playing Atari with Deep Reinforcement Learning. The primary motivation behind DQN was to address the challenges of high-dimensional..."
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