가짜연구소 8기 CS 285 스터디

• 2024.3 - 2024.8
• 미리 강의를 듣고 여기에 간략히 정리
• 스터디 시간에 질문/코멘트
• Homework 풀이?

차례

• Lecture 1: Introduction and Course Overview
• Lecture 2: Supervised Learning of Behaviors
• Lecture 3: PyTorch Tutorial
• Lecture 4: Introduction to Reinforcement Learning
• Lecture 5: Policy Gradients
• Lecture 6: Actor-Critic Algorithms
• Lecture 7: Value Function Methods
• Lecture 8: Deep RL with Q-Functions
• Lecture 9: Advanced Policy Gradients
• Lecture 10: Optimal Control and Planning
• Lecture 11: Model-Based Reinforcement Learning
• Lecture 12: Model-Based Policy Learning
• Lecture 13: Exploration (Part 1)
• Lecture 14: Exploration (Part 2)
• Lecture 15: Offline Reinforcement Learning (Part 1)
• Lecture 16: Offline Reinforcement Learning (Part 2)
• Lecture 17: Reinforcement Learning Theory Basics
• Lecture 18: Variational Inference and Generative Models
• Lecture 19: Connection between Inference and Control
• Lecture 20: Inverse Reinforcement Learning
• Lecture 21: RL with Sequence Models
• Lecture 22: Meta-Learning and Transfer Learning
• Lecture 23: Challenges and Open Problems