Delves into Reinforcement Learning with Human Feedback, discussing convergence of estimators and introducing a pessimistic approach for improved performance.
Explores mastery learning, behaviorism, and instructional design, emphasizing personalized instruction and the effectiveness of Intelligent Tutoring Systems.
Explores training robots through reinforcement learning and learning from demonstration, highlighting challenges in human-robot interaction and data collection.
Introduces reinforcement learning, covering its definitions, applications, and theoretical foundations, while outlining the course structure and objectives.
Covers model-free prediction methods in reinforcement learning, focusing on Monte Carlo and Temporal Differences for estimating value functions without transition dynamics knowledge.
Explores model-based deep reinforcement learning, focusing on Monte Carlo Tree Search and its applications in game strategies and decision-making processes.
