Intelligent Agents: Making Decisions and Planning

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Explores fundamental principles in scientific research, the impact of computers, numerical algorithms, and deep learning in solving high-dimensional problems.

Perception: Data-Driven Approaches

Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.

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Compares model-based and model-free reinforcement learning, highlighting the advantages of the former in adapting to reward changes and planning future actions.

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Introduces Q-Learning, Deep Q-Learning, REINFORCE algorithm, and Monte-Carlo Tree Search in reinforcement learning, culminating in AlphaGo Zero.

Deep Learning Agents: Reinforcement Learning

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