Reinforcement Learning: Basics

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Provides insights on gradient-based algorithms, deep learning mysteries, and the challenges of non-convex problems.

Covers the basics of reinforcement learning, including trial-and-error learning, Q-learning, deep RL, and applications in gaming and planning.

Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.

Covers stylized facts of asset returns, summary statistics, testing for normality, Q-Q plots, and efficient market hypothesis.

Covers key concepts in reinforcement learning, neural networks, clustering, and unsupervised learning, emphasizing their applications and challenges.

Explores reinforcement learning in AI to master games using neural networks.

Explores artificial neural networks, reward information in the brain, animal conditioning, deep reinforcement learning, and a quiz on rewards.

Explores the gap between AI and human intelligence through neuroscience-inspired models and algorithms.

Covers learning by rewards in deep reinforcement learning without math details.