Description
Fundamentals of Reinforcement Learning is a course published by Udemy Online Academy. This course provides a comprehensive introduction to one of the major branches of machine learning, in which intelligent agents learn optimal behavior through interaction with an environment. Designed for individuals, AI professionals, and developers, this course covers the theoretical foundations and practical implementations of reinforcement learning algorithms used in robotics, game AI, autonomous systems, and decision-making applications. Students will learn key concepts such as Markov Decision Processes (MDPs), rewards, policies, value functions, exploration versus exploitation, dynamic programming, Monte Carlo methods, temporal difference learning (TD), and Q-learning.
This course explains the fundamentals of this exciting branch of artificial intelligence. You will learn the theory behind the algorithms and learn how to implement them in Python. There are plenty of opportunities to do this in this course, and each section comes with a coding assignment where you will build your own algorithms. By the end of this course, you will have a basic understanding of these algorithms.
What you will learn in Fundamentals of Reinforcement Learning:
- Learn the core concepts of reinforcement learning, from k-armed bandits to advanced programming algorithms.
- Implement key reinforcement learning algorithms, including Monte Carlo, SARSA, and Q-learning, from scratch in Python.
- Apply reinforcement learning techniques to solve classic problems such as frozen lakes, jack-in-the-box, blackjack, and cliff walking.
- Develop a deep understanding of the mathematical foundations underlying modern reinforcement learning approaches.
- And…
Course specifications
Publisher: Udemy Instructors: Tom Walker Language: English Level: Intermediate Number of Lessons: 52 Duration: 10 hours and 39 minutes
Course topics
Fundamentals of Reinforcement Learning Prerequisites
Students should be comfortable with Python programming, including NumPy and Pandas. Basic understanding of probability concepts is beneficial (probability distributions, random variables, conditional and joint probabilities) While familiarity with other machine learning methods is helpful, it’s not required. We’ll build the necessary reinforcement learning concepts from the ground up. Section assignments are in pure python (rather than Jupyter Notebooks), and often span edits to multiple modules, so students should be setup with an editor (e.g. VS Code or PyCharm)
Pictures
Fundamentals of Reinforcement Learning introduction video
Installation guide
After Extract, watch with your favorite Player.
Subtitle: None
Quality: 1080p
Downloadly link
Rapidgator link
File password (s): www.downloadly.ir
Size
3.6 GB
