Lecture 8: Markov Decision Processes - Reinforcement Learning | Stanford CS221: AI (Autumn 2019)
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/2Zv1JpK
Topics: Reinforcement learning, Monte Carlo, SARSA, Q-learning, Exploration/exploitation, function approximation
Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor - Stanford University
http://onlinehub.stanford.edu/
Associate Professor Percy Liang
Associate Professor of Computer Science and Statistics (courtesy)
https://profiles.stanford.edu/percy-liang
Assistant Professor Dorsa Sadigh
Assistant Professor in the Computer Science Department & Electrical Engineering Department
https://profiles.stanford.edu/dorsa-sadigh
To follow along with the course schedule and syllabus, visit:
https://stanford-cs221.github.io/autumn2019/#schedule
Видео Lecture 8: Markov Decision Processes - Reinforcement Learning | Stanford CS221: AI (Autumn 2019) канала stanfordonline
Topics: Reinforcement learning, Monte Carlo, SARSA, Q-learning, Exploration/exploitation, function approximation
Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor - Stanford University
http://onlinehub.stanford.edu/
Associate Professor Percy Liang
Associate Professor of Computer Science and Statistics (courtesy)
https://profiles.stanford.edu/percy-liang
Assistant Professor Dorsa Sadigh
Assistant Professor in the Computer Science Department & Electrical Engineering Department
https://profiles.stanford.edu/dorsa-sadigh
To follow along with the course schedule and syllabus, visit:
https://stanford-cs221.github.io/autumn2019/#schedule
Видео Lecture 8: Markov Decision Processes - Reinforcement Learning | Stanford CS221: AI (Autumn 2019) канала stanfordonline
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
Lecture 7: Markov Decision Processes - Value Iteration | Stanford CS221: AI (Autumn 2019)Lecture 5: Search 1 - Dynamic Programming, Uniform Cost Search | Stanford CS221: AI (Autumn 2019)Deep Q-Learning - Combining Neural Networks and Reinforcement LearningLecture 17 - MDPs & Value/Policy Iteration | Stanford CS229: Machine Learning (Autumn 2018)Lecture 1: Overview | Stanford CS221: AI (Autumn 2019)Artificial intelligence and algorithms: pros and cons | DW Documentary (AI documentary)Markov Decision Processes (MDPs) - Structuring a Reinforcement Learning ProblemLecture 9: Game Playing 1 - Minimax, Alpha-beta Pruning | Stanford CS221: AI (Autumn 2019)Origin of Markov chains | Journey into information theory | Computer Science | Khan AcademyLecture 2: Machine Learning 1 - Linear Classifiers, SGD | Stanford CS221: AI (Autumn 2019)Markov Decision ProcessesHidden Markov Model | Part 1RL Course by David Silver - Lecture 2: Markov Decision ProcessMarkov Chains Clearly Explained! Part - 1Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 1 - IntroductionBuilding a Custom Environment for Deep Reinforcement Learning with OpenAI Gym and PythonPolicy and Value IterationAn Introduction to Markov Decision Processes and Reinforcement Learning