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Explore deep recurrent Q-networks in this 28-minute lecture, delving into the fundamentals of recurrent neural networks, LSTM units, and their application in Q-learning. Gain insights into the structure and functionality of gates, unrolling techniques, and how these concepts are integrated into recurrent Q networks. Build upon previous knowledge with a comprehensive recap before diving into advanced topics in reinforcement learning and neural network architectures.
