Deep reinforcement learning is a machine learning technique that combines reinforcement learning with deep neural networks.
In deep reinforcement learning, an agent learns to make decisions through feedback received from the environment, but instead of using classical learning techniques, a deep neural network is used to learn the optimal decision policy. The deep neural network takes as input the data from the environment and produces as output the action that the agent should take at that moment.
Deep reinforcement learning is a very powerful technique for learning complex and unstructured tasks, such as robot control or decision-making in complex games. In addition, it has been shown that deep reinforcement learning can be used to learn to play complex strategy games, such as Go or Chess, outperforming the best human players.
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