Deep reinforcement learning is a subfield of machine learning that combines reinforcement learning principles with deep learning techniques to enable agents to learn how to make decisions through trial and error in complex environments. It focuses on using neural networks to approximate the value functions or policy functions, which helps agents optimize their actions based on received rewards. This integration allows for more efficient learning and improved performance in tasks that involve high-dimensional state spaces.
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