Artificial Intelligence IV - Reinforcement Learning in Java
Artificial Intelligence IV - Reinforcement Learning in Java
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | 39 lectures | 3h 2m Duration | 979.51 MB
Genre: eLearning | Language: English
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | 39 lectures | 3h 2m Duration | 979.51 MB
Genre: eLearning | Language: English
All you need to know about Markov Decision processes, value- and policy-iteation as well as about Q learning approach
This course is about Reinforcement Learning. The first step is to talk about the mathematical background: we can use a Markov Decision Process as a model for reinforcement learning. We can solve the problem 3 ways: value-iteration, policy-iteration and Q-learning. Q-learning is a model free approach so it is state-of-the-art approach. It learns the optimal policy by interacting with the environment. So these are the topics:
Markov Decision Processes
value-iteration and policy-iteration
Q-learning fundamentals
pathfinding algorithms with Q-learning
Q-learning with neural networks