Reinforcement Learning beginner to master – AI in Python

Reinforcement Learning beginner to master – AI in Python, available at $84.99, has an average rating of 4.57, with 132 lectures, based on 1093 reviews, and has 8734 subscribers.

You will learn about Understand the Reinforcement Learning paradigm and the tasks that it's best suited to solve. Understand the process of solving a cognitive task using Reinforcement Learning Understand the different approaches to solving a task using Reinforcement Learning and choose the most fitting Implement Reinforcement Learning algorithms completely from scratch Fundamentally understand the learning process for each algorithm Debug and extend the algorithms presented Understand and implement new algorithms from research papers This course is ideal for individuals who are Developers who want to get a job in Machine Learning or Data scientists/analysts and ML practitioners seeking to expand their breadth of knowledge. or Researchers/scholars seeking to enhance their practical coding skills. It is particularly useful for Developers who want to get a job in Machine Learning or Data scientists/analysts and ML practitioners seeking to expand their breadth of knowledge. or Researchers/scholars seeking to enhance their practical coding skills.

Enroll now: Reinforcement Learning beginner to master – AI in Python

Summary

Title: Reinforcement Learning beginner to master – AI in Python

Price: $84.99

Average Rating: 4.57

Number of Lectures: 132

Number of Published Lectures: 132

Number of Curriculum Items: 132

Number of Published Curriculum Objects: 132

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Understand the Reinforcement Learning paradigm and the tasks that it's best suited to solve.
Understand the process of solving a cognitive task using Reinforcement Learning
Understand the different approaches to solving a task using Reinforcement Learning and choose the most fitting
Implement Reinforcement Learning algorithms completely from scratch
Fundamentally understand the learning process for each algorithm
Debug and extend the algorithms presented
Understand and implement new algorithms from research papers

Who Should Attend

Developers who want to get a job in Machine Learning
Data scientists/analysts and ML practitioners seeking to expand their breadth of knowledge.
Researchers/scholars seeking to enhance their practical coding skills.

Target Audiences

Developers who want to get a job in Machine Learning
Data scientists/analysts and ML practitioners seeking to expand their breadth of knowledge.
Researchers/scholars seeking to enhance their practical coding skills.

This is the most complete Reinforcement Learning course on Udemy. In it you will learn the basics of Reinforcement Learning, one of the three paradigms of modern artificial intelligence. You will implement from scratch adaptive algorithms that solve control tasks based on experience. You will also learn to combine these algorithms with Deep Learning techniques and neural networks, giving rise to the branch known as Deep Reinforcement Learning.

This course will give you the foundation you need to be able to understand new algorithms as they emerge. It will also prepare you for the next courses in this series, in which we will go much deeper into different branches of Reinforcement Learning and look at some of the more advanced algorithms that exist.

The course is focused on developing practical skills. Therefore, after learning the most important concepts of each family of methods, we will implement one or more of their algorithms in jupyter notebooks, from scratch.

This course is divided into three parts and covers the following topics:

Part 1 (Tabular methods):

– Markov decision process

– Dynamic programming

– Monte Carlo methods

– Time difference methods (SARSA, Q-Learning)

– N-step bootstrapping

Part 2 (Continuous state spaces):

– State aggregation

– Tile Coding

Part 3 (Deep Reinforcement Learning):

– Deep SARSA

– Deep Q-Learning

– REINFORCE

– Advantage Actor-Critic / A2C (Advantage Actor-Critic / A2C method)

Course Curriculum

Chapter 1: Welcome module

Lecture 1: [IMPORTANT] English captions available for sections 1-4

Lecture 2: Welcome

Lecture 3: Reinforcement Learning series

Lecture 4: Course structure

Lecture 5: Environment setup [Important]

Lecture 6: Connect with us on social media

Chapter 2: The Markov decision process (MDP)

Lecture 1: Elements common to all control tasks

Lecture 2: The Markov decision process (MDP)

Lecture 3: Types of Markov decision process

Lecture 4: Trajectory vs episode

Lecture 5: Reward vs Return

Lecture 6: Discount factor

Lecture 7: Policy

Lecture 8: State values v(s) and action values q(s,a)

Lecture 9: Bellman equations

Lecture 10: Solving a Markov decision process

Lecture 11: Setup – MDP in code

Lecture 12: MDP in code – Part 1

Lecture 13: MDP in code – Part 2

Chapter 3: Dynamic Programming

Lecture 1: Introduction to Dynamic Programming

Lecture 2: Value iteration

Lecture 3: Setup – Value iteration

Lecture 4: Coding – Value iteration 1

Lecture 5: Coding – Value iteration 2

Lecture 6: Coding – Value iteration 3

Lecture 7: Coding – Value iteration 4

Lecture 8: Coding – Value iteration 5

Lecture 9: Policy iteration

Lecture 10: Policy evaluation

Lecture 11: Setup – Policy iteration

Lecture 12: Coding – Policy iteration 1

Lecture 13: Coding – Policy iteration 2

Lecture 14: Policy Improvement

Lecture 15: Coding – Policy iteration 3

Lecture 16: Coding – Policy iteration 4

Lecture 17: Policy iteration in practice

Lecture 18: Generalized Policy Iteration (GPI)

Chapter 4: Monte Carlo methods

Lecture 1: Monte Carlo methods

Lecture 2: Solving control tasks with Monte Carlo methods

Lecture 3: On-policy Monte Carlo control

Lecture 4: Setup – On-policy Monte Carlo control

Lecture 5: Coding – On-policy Monte Carlo control 1

Lecture 6: Coding – On-policy Monte Carlo control 2

Lecture 7: Coding – On-policy Monte Carlo control 3

Lecture 8: Setup – Constant alpha Monte Carlo

Lecture 9: Coding – Constant alpha Monte Carlo

Lecture 10: Off-policy Monte Carlo control

Lecture 11: Setup – Off-policy Monte Carlo control

Lecture 12: Coding – Off-policy Monte Carlo 1

Lecture 13: Coding – Off-policy Monte Carlo 2

Lecture 14: Coding – Off-policy Monte Carlo 3

Chapter 5: Temporal difference methods

Lecture 1: Temporal difference methods

Lecture 2: Solving control tasks with temporal difference methods

Lecture 3: Monte Carlo vs temporal difference methods

Lecture 4: SARSA

Lecture 5: Setup – SARSA

Lecture 6: Coding – SARSA 1

Lecture 7: Coding – SARSA 2

Lecture 8: Q-Learning

Lecture 9: Setup – Q-Learning

Lecture 10: Coding – Q-Learning 1

Lecture 11: Coding – Q-Learning 2

Lecture 12: Advantages of temporal difference methods

Chapter 6: N-step bootstrapping

Lecture 1: N-step temporal difference methods

Lecture 2: Where do n-step methods fit?

Lecture 3: Effect of changing n

Lecture 4: N-step SARSA

Lecture 5: N-step SARSA in action

Lecture 6: Setup – n-step SARSA

Lecture 7: Coding – n-step SARSA

Chapter 7: Continuous state spaces

Lecture 1: Setup – Classic control tasks

Lecture 2: Coding – Classic control tasks

Lecture 3: Working with continuous state spaces

Lecture 4: State aggregation

Lecture 5: Setup – Continuous state spaces

Lecture 6: Coding – State aggregation 1

Lecture 7: Coding – State aggregation 2

Lecture 8: Coding – State aggregation 3

Lecture 9: Tile coding

Lecture 10: Coding – Tile coding 1

Lecture 11: Coding – Tile coding 2

Lecture 12: Coding – Tile coding 3

Chapter 8: Brief introduction to neural networks

Lecture 1: Function approximators

Lecture 2: Artificial Neural Networks

Lecture 3: Artificial Neurons

Lecture 4: How to represent a Neural Network

Lecture 5: Stochastic Gradient Descent

Lecture 6: Neural Network optimization

Chapter 9: Deep SARSA

Lecture 1: Deep SARSA

Lecture 2: Neural Network optimization (Deep Q-Network)

Lecture 3: Experience Replay

Instructors

Escape Velocity Labs
Hands-on, comprehensive AI courses

Rating Distribution

1 stars: 26 votes
2 stars: 17 votes
3 stars: 116 votes
4 stars: 342 votes
5 stars: 592 votes

Frequently Asked Questions

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