Deep Reinforcement Learning | UC Berkeley CS294-112

Comprehensive course on deep reinforcement learning, taught by a leading expert. Hands-on assignments and projects to apply the concepts.

Introduction

CS 285 at UC Berkeley is a course on Deep Reinforcement Learning. It covers a wide range of topics in deep reinforcement learning, including supervised learning of behaviors, introduction to reinforcement learning, policy gradients, actor-critic algorithms, value function methods, Q-learning, advanced policy gradients, model-based learning, and imitating optimal controllers.

Highlights

• Comprehensive coverage of deep reinforcement learning topics
• Taught by Sergey Levine, a leading expert in the field
• Hands-on assignments and projects to apply the concepts
• Lecture recordings and materials from past offerings available

Recommendation

This course is highly recommended for anyone interested in deep reinforcement learning, whether you are a student, researcher, or industry practitioner. It provides a solid foundation in the core concepts and techniques, and the hands-on assignments allow you to apply the knowledge to practical problems.