Deep Reinforcement Learning | NUS CS 6101 Course

Explore the fundamentals of deep reinforcement learning, including algorithms, frameworks, and practical applications in areas like game playing, robotics, and resource management.

Introduction

This course provides an in-depth exploration of deep reinforcement learning, covering the fundamental concepts, algorithms, and practical applications of this powerful machine learning technique. Students will learn how to design and implement deep reinforcement learning models to solve complex problems in various domains.

Highlights

• Comprehensive coverage of deep reinforcement learning algorithms, including Deep Q-Networks, Policy Gradients, and Actor-Critic methods
• Hands-on experience with implementing deep reinforcement learning models using popular frameworks like TensorFlow and PyTorch
• Exposure to cutting-edge research and applications of deep reinforcement learning in areas such as game playing, robotics, and resource management

Recommendation

This course is highly recommended for students and professionals interested in advancing their skills in machine learning, artificial intelligence, and reinforcement learning. It is particularly well-suited for those seeking to apply deep learning techniques to solve complex, sequential decision-making problems.

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