Deep reinforcement learning is a subset of machine learning that combines reinforcement learning principles with deep learning architectures. It allows an agent to learn how to make decisions by interacting with an environment, optimizing a reward signal through trial and error. This technique is crucial for creating intelligent systems that can adapt and improve over time, especially in complex environments like autonomous vehicles.
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