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Deep Reinforcement Learning

Practical AI

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From Physics to Deep Reinforcement Learning

This chapter explores the speaker's journey from an academic background in physics to deep reinforcement learning and robotics. It highlights the pivotal role of simulation, the trial-and-error nature of reinforcement learning, and the importance of adapting behavior based on feedback. Key advancements in deep reinforcement learning and the evolution of algorithm efficiency, including the shift to parallel processing, are also discussed.

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