Artificial Intelligence IV – Reinforcement Learning in Java

Artificial Intelligence IV – Reinforcement Learning in Java, available at $64.99, has an average rating of 4.25, with 44 lectures, 4 quizzes, based on 186 reviews, and has 2058 subscribers.

You will learn about Understand reinforcement learning Understand Markov Decision Processes Understand value- and policy-iteration Understand Q-learning approach and it's applications This course is ideal for individuals who are Anyone who wants to understand artificial intelligence and reinforcement learning! It is particularly useful for Anyone who wants to understand artificial intelligence and reinforcement learning!.

Enroll now: Artificial Intelligence IV – Reinforcement Learning in Java

Summary

Title: Artificial Intelligence IV – Reinforcement Learning in Java

Price: $64.99

Average Rating: 4.25

Number of Lectures: 44

Number of Quizzes: 4

Number of Published Lectures: 39

Number of Published Quizzes: 4

Number of Curriculum Items: 48

Number of Published Curriculum Objects: 43

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Understand reinforcement learning
Understand Markov Decision Processes
Understand value- and policy-iteration
Understand Q-learning approach and it's applications

Who Should Attend

Anyone who wants to understand artificial intelligence and reinforcement learning!

Target Audiences

Anyone who wants to understand artificial intelligence and reinforcement learning!

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

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Types of learning

Lecture 3: Applications of reinforcement learning

Chapter 2: Markov Decision Process (MDP) Theory

Lecture 1: Markov decision processes basics I

Lecture 2: Markov decision processes basics II

Lecture 3: Markov decision processes – equations

Lecture 4: Markov decision processes – illustration

Lecture 5: Bellman-equation

Lecture 6: How to solve MDP problems?

Lecture 7: Mathematical formulation of reinforcement learning

Chapter 3: Markov Decision Process – Value Iteration

Lecture 1: What is value iteration?

Lecture 2: Value iteration implementation I

Lecture 3: Value iteration implementation II

Lecture 4: Value iteration implementation III

Lecture 5: Value iteration implementation IV

Lecture 6: Value iteration implementation V

Chapter 4: Markov Decision Process – Policy Iteration

Lecture 1: What is policy iteration?

Lecture 2: Value iteration vs policy iteration

Chapter 5: Q Learning Theory

Lecture 1: Q learning introduction

Lecture 2: Q learning introduction – the algorithm

Lecture 3: Q learning illustration

Lecture 4: Mathematical formulation of Q learning

Chapter 6: Pathfinding with Q-Learning

Lecture 1: —- PATHFINDING —-

Lecture 2: Pathfinding with Q-learning I

Lecture 3: Pathfinding with Q-learning II

Lecture 4: Pathfinding with Q-learning III

Lecture 5: Pathfinding with Q-learning IV

Lecture 6: —- SHORTEST PATH —-

Lecture 7: Shortest path with Q-learning

Chapter 7: Exploration vs. Exploitation Problem

Lecture 1: Exploration vs exploitation problem

Lecture 2: N-armed bandit problem introduction

Lecture 3: N-armed bandit problem implementation I

Lecture 4: N-armed bandit problem implementation II

Lecture 5: Applications: A/B testing in marketing

Chapter 8: Deep Reinforcement Learning Theory

Lecture 1: What is deep Q learning?

Lecture 2: Deep Q learning and ε-greedy strategy

Lecture 3: Deep Q-learning introduction – remember and replay

Lecture 4: Mathematical formulation of deep Q learning

Chapter 9: Course Materials (DOWNLOADS)

Lecture 1: Course materials

Instructors

Holczer Balazs
Software Engineer

Rating Distribution

1 stars: 0 votes
2 stars: 1 votes
3 stars: 20 votes
4 stars: 76 votes
5 stars: 89 votes

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