Introduction to Reinforcement Learning | Machine Learning, AI, Sequential Decision-Making

Explore the fundamentals of reinforcement learning, including Markov decision processes, value functions, and policy optimization. Hands-on exercises using OpenAI Gym.

Introduction

This course provides an introduction to the field of reinforcement learning, which is a powerful approach to sequential decision-making problems. Reinforcement learning has been successfully applied to a wide range of domains, including game-playing, robotics, and natural language processing.

Highlights

• Covers the fundamental concepts and algorithms of reinforcement learning, including Markov decision processes, value functions, and policy optimization.
• Includes hands-on exercises and demonstrations using the popular OpenAI Gym environment.
• Taught by leading experts in the field from University College London (UCL) and DeepMind.

Recommendation

This course is recommended for anyone interested in machine learning, artificial intelligence, or sequential decision-making problems. It is suitable for students, researchers, and professionals with a background in computer science, mathematics, or a related field.

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