Deep reinforcement learning is a type of machine learning that combines reinforcement learning principles with deep learning techniques. In this approach, agents learn to make decisions by receiving feedback from their environment, typically in the form of rewards or penalties, while using deep neural networks to process and interpret complex data inputs. This method enables systems to improve their performance over time through trial and error, making it particularly powerful for tasks that require sequential decision-making.
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