Deep Q Network (DQN)

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• Deep Reinforcement Learning (DRL)
• Reinforcement Learning (RL)
• Model Free Reinforcement learning algorithms (Monte Carlo, SARSA, Q-learning) | Madhu Sanjeevi (Mady) - Medium
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When feedback is provided, it might be long time after the fateful decision has been made. In reality, the feedback is likely to be the result of a large number of prior decisions, taken amid a shifting, uncertain environment. Unlike supervised learning, there are no correct input/output pairs, so suboptimal actions are not explicitly corrected, wrong actions just decrease the corresponding value in the Q-table, meaning there’s less chance choosing the same action should the same state be encountered again. Quora | Jaron Collis

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