Reinforcement Learning for Pacman

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (30)

Page 1 of 3

Introduction to Reinforcement Learning: Key Concepts and Applications

Introduces reinforcement learning, covering its definitions, applications, and theoretical foundations, while outlining the course structure and objectives.

Reinforcement Learning: Basics and Applications

Covers the basics of reinforcement learning, including Markov Decision Processes and policy gradient methods, and explores real-world applications and recent advances.

Markov Decision Processes: Foundations of Reinforcement Learning

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

Reinforcement Learning for Pacman

Explores applying reinforcement learning to teach Pacman to play autonomously using policy gradient methods and Markov decision processes.

Interactive Lecture: Reinforcement Learning

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

Introduction to Reinforcement Learning: Concepts and Applications

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

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 Concepts

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

Policy Gradient Methods: Binary Actor Example

Introduces policy gradient methods using a simple example of a single neuron with binary output.

Markov Games: Concepts and Applications in Reinforcement Learning

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