Deep Reinforcement Learning
Deep Reinforcement Learning is a subfield of Artificial Intelligence and Machine Learning that involves training an agent to make decisions in an environment by maximizing a reward signal. It combines Reinforcement Learning, which is a type of Machine Learning that involves an agent learning to interact with an environment to achieve a goal, with Deep Learning, which is a subset of Machine Learning that involves training artificial neural networks to learn from data. Deep Reinforcement Learning algorithms use neural networks to approximate the optimal policy, or decision-making strategy, for an agent in a given environment. These algorithms have been successfully applied to a variety of tasks, including game playing, robotics, and natural language processing.
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