Reinforcement Learning- The Complete Course in 2022

Reinforcement Learning: The Complete Course in 2022, available at $19.99, has an average rating of 4.85, with 169 lectures, based on 63 reviews, and has 382 subscribers.

You will learn about Policy gradient algorithm Markov Chain Policy iteration algorithm Monte Carlo method Q-Learning Deep-Q networks Double Deep-Q networks SARSA algorithm Duelling Deep-Q networks REINFORCE algorithm Actor-critic algorithm Understand a cutting-edge implementation of the A2C algorithm (OpenAI Baselines) Deep Recurrent Q-Learning algorithm and DRQN agent Implementation Asynchronous Advantage Actor-Critic algorithm and A3C agent Implementation Proximal Policy Optimization algorithm and PPO agent Implementation Deep Deterministic Policy Gradient algorithm and DDPG agent Implementation This course is ideal for individuals who are Anyone interested in Deep Learning, Machine Learning and Artificial Intelligence or Students who have at least high school knowledge in math and who want to start learning Machine Learning, Deep Learning, and Artificial Intelligence or Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning, Deep Learning, Artificial Intelligence. or Any people who are not that comfortable with coding but who are interested in Machine Learning, Deep Learning, Artificial Intelligence and want to apply it easily on datasets. or Any students in college who want to start a career in Data Science or Any data analysts who want to level up in Machine Learning, Deep Learning and Artificial Intelligence. or Any people who are not satisfied with their job and who want to become a Data Scientist. or Any people who want to create added value to their business by using powerful Machine Learning, Artificial Intelligence and Deep Learning tools. Any people who want to work in a Car company as a Data Scientist, Machine Learning, Deep Learning and Artificial Intelligence engineer. or AI experts who want to expand on the field of applications or Data Scientists who want to take their AI Skills to the next level or Students in tech-related programs who want to pursue a career in Data Science, Machine Learning, or Artificial Intelligence or Anyone passionate about Artificial Intelligence or Deep Learning Engineers who want to level up their skills and knowledge or AI experts who want to level up their skills and knowledge It is particularly useful for Anyone interested in Deep Learning, Machine Learning and Artificial Intelligence or Students who have at least high school knowledge in math and who want to start learning Machine Learning, Deep Learning, and Artificial Intelligence or Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning, Deep Learning, Artificial Intelligence. or Any people who are not that comfortable with coding but who are interested in Machine Learning, Deep Learning, Artificial Intelligence and want to apply it easily on datasets. or Any students in college who want to start a career in Data Science or Any data analysts who want to level up in Machine Learning, Deep Learning and Artificial Intelligence. or Any people who are not satisfied with their job and who want to become a Data Scientist. or Any people who want to create added value to their business by using powerful Machine Learning, Artificial Intelligence and Deep Learning tools. Any people who want to work in a Car company as a Data Scientist, Machine Learning, Deep Learning and Artificial Intelligence engineer. or AI experts who want to expand on the field of applications or Data Scientists who want to take their AI Skills to the next level or Students in tech-related programs who want to pursue a career in Data Science, Machine Learning, or Artificial Intelligence or Anyone passionate about Artificial Intelligence or Deep Learning Engineers who want to level up their skills and knowledge or AI experts who want to level up their skills and knowledge.

Enroll now: Reinforcement Learning: The Complete Course in 2022

Summary

Title: Reinforcement Learning: The Complete Course in 2022

Price: $19.99

Average Rating: 4.85

Number of Lectures: 169

Number of Published Lectures: 169

Number of Curriculum Items: 169

Number of Published Curriculum Objects: 169

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will Learn

Policy gradient algorithm

Markov Chain

Policy iteration algorithm

Monte Carlo method

Q-Learning

Deep-Q networks

Double Deep-Q networks

SARSA algorithm

Duelling Deep-Q networks

REINFORCE algorithm

Actor-critic algorithm

Understand a cutting-edge implementation of the A2C algorithm (OpenAI Baselines)

Deep Recurrent Q-Learning algorithm and DRQN agent Implementation

Asynchronous Advantage Actor-Critic algorithm and A3C agent Implementation

Proximal Policy Optimization algorithm and PPO agent Implementation

Deep Deterministic Policy Gradient algorithm and DDPG agent Implementation

Who Should Attend

Anyone interested in Deep Learning, Machine Learning and Artificial Intelligence

Students who have at least high school knowledge in math and who want to start learning Machine Learning, Deep Learning, and Artificial Intelligence

Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning, Deep Learning, Artificial Intelligence.

Any people who are not that comfortable with coding but who are interested in Machine Learning, Deep Learning, Artificial Intelligence and want to apply it easily on datasets.

Any students in college who want to start a career in Data Science

Any data analysts who want to level up in Machine Learning, Deep Learning and Artificial Intelligence.

Any people who are not satisfied with their job and who want to become a Data Scientist.

Any people who want to create added value to their business by using powerful Machine Learning, Artificial Intelligence and Deep Learning tools. Any people who want to work in a Car company as a Data Scientist, Machine Learning, Deep Learning and Artificial Intelligence engineer.

AI experts who want to expand on the field of applications

Data Scientists who want to take their AI Skills to the next level

Students in tech-related programs who want to pursue a career in Data Science, Machine Learning, or Artificial Intelligence

Anyone passionate about Artificial Intelligence

Deep Learning Engineers who want to level up their skills and knowledge

AI experts who want to level up their skills and knowledge

Target Audiences

Anyone interested in Deep Learning, Machine Learning and Artificial Intelligence

Students who have at least high school knowledge in math and who want to start learning Machine Learning, Deep Learning, and Artificial Intelligence

Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning, Deep Learning, Artificial Intelligence.

Any people who are not that comfortable with coding but who are interested in Machine Learning, Deep Learning, Artificial Intelligence and want to apply it easily on datasets.

Any students in college who want to start a career in Data Science

Any data analysts who want to level up in Machine Learning, Deep Learning and Artificial Intelligence.

Any people who are not satisfied with their job and who want to become a Data Scientist.

Any people who want to create added value to their business by using powerful Machine Learning, Artificial Intelligence and Deep Learning tools. Any people who want to work in a Car company as a Data Scientist, Machine Learning, Deep Learning and Artificial Intelligence engineer.

AI experts who want to expand on the field of applications

Data Scientists who want to take their AI Skills to the next level

Students in tech-related programs who want to pursue a career in Data Science, Machine Learning, or Artificial Intelligence

Anyone passionate about Artificial Intelligence

Deep Learning Engineers who want to level up their skills and knowledge

AI experts who want to level up their skills and knowledge

When people talk about artificial intelligence, they usually don’t mean supervised and unsupervised machine learning.

These tasks are pretty trivial compared to what we think of AIs doing – playing chess and Go, driving cars, and beating video games at a superhuman level.

Reinforcement learning has recently become popular for doing all of that and more.

Much like deep learning, a lot of the theory was discovered in the 70s and 80s but it hasn’t been until recently that we’ve been able to observe first hand the amazing results that are possible.

In 2016 we saw Google’s AlphaGo beat the world Champion in Go.

We saw AIs playing video games like Doom and Super Mario.

Self-driving cars have started driving on real roads with other drivers and even carrying passengers (Uber), all without human assistance.

If that sounds amazing, brace yourself for the future because the law of accelerating returns dictates that this progress is only going to continue to increase exponentially.

Learning about supervised and unsupervised machine learning is no small feat.

And yet reinforcement learning opens up a whole new world. As you’ll learn in this course, the reinforcement learning paradigm is more different from supervised and unsupervised learning than they are from each other.

It’s led to new and amazing insights both in behavioural psychology and neuroscience. As you’ll learn in this course, there are many analogous processes when it comes to teaching an agent and teaching an animal or even a human. It’s the closest thing we have so far to a true general artificial intelligence. What’s covered in this course?

Deep Learning.

Google Colab

Anaconda.

Jupiter Notebook.

Activation Function.

Keras.

Pandas.

TensorFlow 2.0

Neural Network

Matplotlib.

scikit-learn.

OpenAI Gym.

Pytorch.

Policy gradient algorithm.

Markov Chain.

Policy iteration algorithm.

Monte Carlo method.

Q-Learning.

Deep-Q networks.

Double Deep-Q networks.

Duelling Deep-Q networks.

REINFORCE algorithm.

The multi-armed bandit problem.

Ways to calculate means and moving averages and their relationship to stochastic gradient descent.

Markov Decision Processes (MDPs).

Dynamic Programming.

Temporal Difference (TD) Learning (Q-Learning and SARSA).

Actor-critic algorithm.

Advantage Actor-Critic (A2C).

Deep Recurrent Q-Learning algorithm and DRQN agent Implementation .

Asynchronous Advantage Actor-Critic algorithm and A3C agent Implementation.

Proximal Policy Optimization algorithm and PPO agent Implementation .

Deep Deterministic Policy Gradient algorithm and DDPG agent Implementation.

Contextual bandits.

If you’re ready to take on a brand new challenge, and learn about AI techniques that you’ve never seen before in traditional supervised machine learning, unsupervised machine learning, or even deep learning, then this course is for you.

Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models. There are five big projects on healthcare problems and one small project to practice. These projects are listed below:

Robot control.

Hill Climbing game.

Atari game.

Frozen Lake environment.

Coin Flipping gamble

Calculating Pi.

Blackjack game.

Windy Gridworld environment playground.

Taxi problem.

The MAB problem.

Mountain car environment.

Online Advertisement.

Cryptocurrency Trading Agents.

Building Stock/Share Trading Agents.

That is all. See you in class!

“If you can’t implement it, you don’t understand it”

Or as the great physicist Richard Feynman said: “What I cannot create, I do not understand”.

My courses are the ONLY course where you will learn how to implement deep REINFORCEMENT LEARNING algorithms from scratch

Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?

After doing the same thing with 10 datasets, you realize you didn’t learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times

Course Curriculum

Chapter 1: Introduction (New Content)

Lecture 1: Course structure

Lecture 2: How To Make The Most Out Of This Course

Lecture 3: What is Reinforcement Learning and Why we need Reinforcement Learning?

Lecture 4: Reward

Lecture 5: Introduction to the agent, environment, action and observation

Lecture 6: What is Tensorflow and how to install it

Lecture 7: Setting up the working environment

Lecture 8: What is OpenAI Gym?

Lecture 9: Anaconda Installation

Chapter 2: (New Content) Robot Control System Using Deep Reinforcement Learning

Lecture 1: Introduction to Robot Control and three laws of Robotics

Lecture 2: Short robotics timeline and Automatic control

Lecture 3: Reinforcement learning basics and Agent-environment interface

Lecture 4: Reinforcement Learning Algorithm

Lecture 5: Keras DQN

Lecture 6: Cart Pole Implementation Part 1

Lecture 7: Cart Pole Implementation Part 2

Lecture 8: Cart Pole Implementation Part 3

Lecture 9: Summary of the project

Chapter 3: (New Content) Playing Atari Games

Lecture 1: Developing a policy gradient algorithm Part 1

Lecture 2: Developing a policy gradient algorithm Part 2

Lecture 3: Developing a policy gradient algorithm Part 3

Lecture 4: Developing a policy gradient algorithm Part 4

Lecture 5: Developing hill climbing part 1

Lecture 6: Developing hill climbing part 2

Lecture 7: Developing hill climbing part 3

Lecture 8: Developing hill climbing part 4

Lecture 9: Simulating Atari environments part 1

Lecture 10: Simulating Atari environments part 2

Chapter 4: (NEW CONTENT) Markov Decision Processes and Dynamic Programming

Lecture 1: Introduction

Lecture 2: Theory about Markov Chain and steps to create Markov Chain

Lecture 3: Creating Markov Chain Part 1

Lecture 4: Creating Markov Chain Part 2

Lecture 5: How does Markov chain work?

Lecture 6: MDP Introduction and steps to create MDP

Lecture 7: MDP Implementation part 1

Lecture 8: MDP Implementation part 2

Lecture 9: How does MDP work?

Lecture 10: Introduction to policy evaluation and steps to create policy evaluation

Lecture 11: Policy evaluation Implementation Part 1

Lecture 12: Policy evaluation Implementation Part 2

Lecture 13: How does Policy evaluation work?

Lecture 14: Policy evaluation Implementation Part 3

Lecture 15: Introduction to Simulating the FrozenLake environment

Lecture 16: Simulating the FrozenLake environment Part 1

Lecture 17: Simulating the FrozenLake environment Part 2

Lecture 18: Simulating the FrozenLake environment Part 3 (How does it work?)

Lecture 19: Simulating the FrozenLake environment Part 4

Lecture 20: Introduction to MDP with a value iteration algorithm and steps to implement it

Lecture 21: Solving an MDP with a value iteration algorithm Part 1

Lecture 22: Solving an MDP with a value iteration algorithm Part 2

Lecture 23: Solving an MDP with a value iteration algorithm Part 3 (How does it work?)

Lecture 24: Solving an MDP with a value iteration algorithm Part 4

Lecture 25: Solving an MDP with a value iteration algorithm Part 5

Lecture 26: Introduction to Solving an MDP with a policy iteration algorithm

Lecture 27: Solving an MDP with a policy iteration algorithm Part 1

Lecture 28: Solving an MDP with a policy iteration algorithm Part 2

Lecture 29: Solving an MDP with a policy iteration algorithm Part 3

Lecture 30: Solving an MDP with a policy iteration algorithm (How does it work?)

Lecture 31: Solving an MDP with a policy iteration algorithm Part 5

Lecture 32: coin-flipping gamble problem Introduction

Lecture 33: coin-flipping gamble problem Part 1

Lecture 34: coin-flipping gamble problem Part 2

Lecture 35: coin-flipping gamble problem Part 3

Lecture 36: coin-flipping gamble problem Part 4

Lecture 37: coin-flipping gamble problem Part 5

Lecture 38: coin-flipping gamble problem (How does it work?)

Lecture 39: coin-flipping gamble problem (How does it work?) continued

Lecture 40: coin-flipping gamble problem Part 6

Chapter 5: (NEW CONTENT) Monte Carlo Methods for Making Numerical Estimations

Lecture 1: Introduction

Lecture 2: Introduction to Calculating Pi using the Monte Carlo method

Lecture 3: Calculating Pi using the Monte Carlo method Implementation Part 1

Lecture 4: Calculating Pi using the Monte Carlo method Implementation Part 2

Lecture 5: Calculating Pi using the Monte Carlo method explanation

Lecture 6: Monte Carlo policy evaluation Introduction

Lecture 7: Monte Carlo policy evaluation Implementation Part 1

Lecture 8: Monte Carlo policy evaluation Implementation Part 2

Lecture 9: Monte Carlo policy evaluation explanation

Lecture 10: Introduction to Playing Blackjack with Monte Carlo prediction

Lecture 11: Playing Blackjack with Monte Carlo prediction Part 1

Lecture 12: Playing Blackjack with Monte Carlo prediction Part 2

Lecture 13: Playing Blackjack with Monte Carlo prediction Part 3

Lecture 14: Playing Blackjack with Monte Carlo prediction Part 4

Lecture 15: Playing Blackjack with Monte Carlo prediction explanation

Lecture 16: On-policy Monte Carlo Control Introduction

Lecture 17: On-policy Monte Carlo Control Implementation Part 1

Lecture 18: On-policy Monte Carlo Control Implementation Part 2

Lecture 19: On-policy Monte Carlo Control Implementation Part 3

Lecture 20: On-policy Monte Carlo Control Implementation Explanation

Lecture 21: MC control with epsilon-greedy policy Introduction

Lecture 22: MC control with epsilon-greedy policy Implementation Part 1

Lecture 23: MC control with epsilon-greedy policy Implementation Part 2

Lecture 24: MC control with epsilon-greedy policy explanation

Lecture 25: Off-policy Monte Carlo control Introduction

Lecture 26: Off-policy Monte Carlo control Implementation part 1

Lecture 27: Off-policy Monte Carlo control Implementation part 2

Instructors

Hoang Quy La
Electrical Engineer

Rating Distribution

1 stars: 10 votes

2 stars: 3 votes

3 stars: 4 votes

4 stars: 2 votes

5 stars: 44 votes

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