Introduction to Reinforcement Learning | Cornell University

Explore the fundamental concepts and algorithms of reinforcement learning, a powerful machine learning approach for optimal decision-making, with hands-on programming assignments.

Introduction

This course provides an introduction to the fundamental concepts and algorithms of reinforcement learning, a machine learning approach that enables an agent to learn optimal behavior through interactions with its environment.

Highlights

• Covers the core principles and techniques of reinforcement learning, including Markov decision processes, dynamic programming, Monte Carlo methods, and temporal-difference learning.
• Explores applications of reinforcement learning in areas such as robotics, game playing, and resource allocation.
• Includes hands-on programming assignments and projects to reinforce the concepts learned.

Recommendation

This course is recommended for students interested in artificial intelligence, machine learning, and decision-making under uncertainty. It provides a solid foundation in reinforcement learning and its practical applications, making it a valuable addition to the curriculum for those pursuing careers in these fields.

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