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Deep Reinforcement Learning

Definition

Deep Reinforcement Learning integrates deep neural networks with reinforcement learning algorithms, enabling systems to acquire optimal actions through trial and error in intricate environments. This methodology permits an agent to discover strategies by interacting with its surroundings, receiving rewards or penalties for its decisions. It utilizes deep learning’s capability to process high-dimensional sensory inputs, such as raw data streams, to inform decision-making policies. The goal is to train autonomous agents proficient in executing complex tasks without explicit programming.