Advanced Machine Learning: Discrete Reinforcement Learning

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Value Iteration Acceleration: PID and Operator Splitting

Explores accelerating the Value Iteration algorithm using control theory and matrix splitting techniques to achieve faster convergence.

Acquiring Data for Learning: Modern Approaches and Challenges

Explores modern approaches and challenges in acquiring data for learning optimal controllers through demonstrations and data-driven methods.

Reinforcement Learning Fundamentals

Delves into the fundamentals of reinforcement learning, discussing states, actions, rewards, policies, and neural network applications.

Learning Agents: Exploration-Exploitation Tradeoff

Explores the exploration-exploitation tradeoff in learning unknown effects of actions using multi-armed bandits and Q-learning.

Mini-Batches in On- and Off-Policy Deep Reinforcement Learning

Explains the significance of mini-batches in Deep Reinforcement Learning and the differences between on-policy and off-policy methods.

Reinforcement Learning: One-step Horizon (Bandit Problems)

Covers Bandit Problems in Reinforcement Learning, focusing on one-step horizon games and Q-values.

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.

Reinforcement Learning for Pacman

Covers the application of reinforcement learning to teach Pacman to play autonomously by trial and error.

Continuous space: action space

Covers methods to transfer techniques from discrete to continuous spaces in reinforcement learning.

Markov Chains: Basics and Applications

Introduces Markov chains, covering basics, generation algorithms, and applications in random walks and Poisson processes.