Introduction to Reinforcement Learning: Key Concepts and Applications

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Markov Decision Processes: Foundations of Reinforcement Learning

Covers Markov Decision Processes, their structure, and their role in reinforcement learning.

Controlled Stochastic Processes

Explores controlled stochastic processes, focusing on analysis, behavior, and optimization, using dynamic programming to solve real-world problems.

Acquiring Data for Learning

Explores training robots through reinforcement learning and learning from demonstration, highlighting challenges in human-robot interaction and data collection.

Interactive Lecture: Reinforcement Learning

Explores advanced reinforcement learning topics, including policies, value functions, Bellman recursion, and on-policy TD control.

Markov Games: Concepts and Applications in Reinforcement Learning

Covers Markov games, their dynamics, equilibria, and applications in reinforcement learning.

Learning and Control for Complex Systems

Explores learning and control for complex systems, addressing challenges and opportunities in technology and interdisciplinary research.

Model-Free Prediction in Reinforcement Learning: Key Methods

Covers model-free prediction methods in reinforcement learning, focusing on Monte Carlo and Temporal Differences for estimating value functions without transition dynamics knowledge.

Value Iteration Acceleration: PID and Operator Splitting

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

Reinforcement Learning for Pacman

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

Introduction to Reinforcement Learning: Concepts and Applications

Introduces reinforcement learning, covering its concepts, applications, and key algorithms.