Deep reinforcement learning is a type of machine learning that combines deep learning with reinforcement learning principles, enabling agents to learn how to make decisions by interacting with their environment. This approach uses neural networks to approximate value functions or policies, allowing the agent to process high-dimensional input and improve its performance through trial and error. By receiving rewards or penalties based on its actions, the agent gradually learns optimal strategies for achieving its goals.
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