HOT
blank

Python Reinforcement Learning, Deep Q-Learning and TRFL

Development

Python Reinforcement Learning, Deep Q-Learning and TRFL, available at $39.99, has an average rating of 2.8, with 77 lectures, 4 quizzes, based on 13 reviews, and has 111 subscribers.

Rating2.8
Enroll Now

You will learn about Implement state-of-the-art Reinforcement Learning algorithms from the basics Discover various techniques of Reinforcement Learning such as MDP, Q Learning, and more Dive into Temporal Difference Learning, an algorithm that combines Monte Carlo methods and dynamic programming Create a virtual Self Driving Car application with Deep Q-Learning Teach a Reinforcement Learning model to play a game using TensorFlow and the OpenAI gym Build projects with TRFL and TensorFlow and integrate essential RL building blocks into existing code Discover improvements to RL algorithms such as DQN and DDPG with TRFL blocks—for example, advanced target network updating, Double Q Learning, and Distributional Q Learning Modify RL agents to include multistep reward techniques such as TD lambda Create TRFL-based RL agents with classic RL methods such as TD Learning, Q Learning, and SARSA This course is ideal for individuals who are This course is designed for AI engineers, Machine Learning engineers, aspiring Reinforcement Learning and Data Science professionals keen to extend their skill set to Reinforcement Learning using Python. It is particularly useful for This course is designed for AI engineers, Machine Learning engineers, aspiring Reinforcement Learning and Data Science professionals keen to extend their skill set to Reinforcement Learning using Python.

Enroll now: Python Reinforcement Learning, Deep Q-Learning and TRFL

Summary

Title: Python Reinforcement Learning, Deep Q-Learning and TRFL

Price: $39.99

Average Rating: 2.8

Number of Lectures: 77

Number of Quizzes: 4

Number of Published Lectures: 77

Number of Published Quizzes: 4

Number of Curriculum Items: 81

Number of Published Curriculum Objects: 81

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

  • Implement state-of-the-art Reinforcement Learning algorithms from the basics
  • Discover various techniques of Reinforcement Learning such as MDP, Q Learning, and more
  • Dive into Temporal Difference Learning, an algorithm that combines Monte Carlo methods and dynamic programming
  • Create a virtual Self Driving Car application with Deep Q-Learning
  • Teach a Reinforcement Learning model to play a game using TensorFlow and the OpenAI gym
  • Build projects with TRFL and TensorFlow and integrate essential RL building blocks into existing code
  • Discover improvements to RL algorithms such as DQN and DDPG with TRFL blocks—for example, advanced target network updating, Double Q Learning, and Distributional Q Learning
  • Modify RL agents to include multistep reward techniques such as TD lambda
  • Create TRFL-based RL agents with classic RL methods such as TD Learning, Q Learning, and SARSA

Who Should Attend

  • This course is designed for AI engineers, Machine Learning engineers, aspiring Reinforcement Learning and Data Science professionals keen to extend their skill set to Reinforcement Learning using Python.

Target Audiences

  • This course is designed for AI engineers, Machine Learning engineers, aspiring Reinforcement Learning and Data Science professionals keen to extend their skill set to Reinforcement Learning using Python.

Reinforcement Learning (RL), allows you to develop smart, quick and self-learning systems in your business surroundings. It is an effective method to train your learning agents and solve a variety of problems in Artificial Intelligence—from games, self-driving cars and robots to enterprise applications that range from data centre energy saving (cooling data centres) to smart warehousing solutions.

This course covers the major advancements and successes achieved in deep reinforcement learning by synergizing deep neural network architectures with reinforcement learning. You will be introduced to the concept of Reinforcement Learning, its advantages and why it’s gaining so much popularity. This course also discusses on Markov Decision Process (MDPs), Monte Carlo tree searches, dynamic programmings such as policy and value iteration, temporal difference learning such as Q-learning and SARSA. You will learn to build convolutional neural network models using TensorFlow and Keras. You will also learn the use of artificial intelligence in a gaming environment with the help of OpenAI Gym.

By the end of this course, you will explore reinforcement learning and will have hands-on experience with real data and artificial intelligence (AI) to build intelligent systems.

Meet Your Expert(s):

We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:

● Lauren Washington is currently the Lead Data Scientist and Machine Learning Developer for smartQED, an AI driven start-up. Lauren worked as a Data Scientist for Topix, Payments Risk Strategist for Google (Google Wallet/Android Pay), Statistical Analyst for Nielsen, and Big Data Intern for the National Opinion Research Center through the University of Chicago. Lauren is also passionate about teaching Machine Learning. She’s currently giving back to the data science community as a Thinkful Data Science Bootcamp Mentor  and a Packt Publishing technical video reviewer. She also earned a Data Science certificate from General Assembly San Francisco (2016), a MA in the Quantitative Methods in the Social Sciences (Applied Statistical Methods) from Columbia University (2012), and a BA in Economics from Spelman College (2010). Lauren is a leader in AI, in Silicon Valley, with a passion for knowledge gathering and sharing.

● Kaiser Hamid Rabbi is a Data Scientist who is super-passionate about Artificial Intelligence, Machine Learning, and Data Science. He has entirely devoted himself to learning more about Big Data Science technologies such as Python, Machine Learning, Deep Learning, Artificial Intelligence, Reinforcement Learning, Data Mining, Data Analysis, Recommender Systems and so on over the last 4 years. Kaiser also has a huge interest in Lygometry (things we know we do not know!) and always tries to understand domain knowledge based on his project experience as much as possible.

● Colibri Digital is a technology consultancy company founded in 2015 by James Cross and Ingrid Funie. The company works to help its clients navigate the rapidly changing and complex world of emerging technologies, with deep expertise in areas such as big data, data science, machine learning, and Cloud computing. Over the past few years, they have worked with some of the World’s largest and most prestigious companies, including a tier 1 investment bank, a leading management consultancy group, and one of the World’s most popular soft drinks companies, helping each of them to better make sense of its data, and process it in more intelligent ways. The company lives by its motto: Data -> Intelligence -> Action.

● Jim DiLorenzo is a freelance programmer and Reinforcement Learning enthusiast. He graduated from Columbia University and is working on his Masters in Computer Science. He has used TRFL in his own RL experiments and when implementing scientific papers into code.

Course Curriculum

Chapter 1: Practical Reinforcement Learning – Agents and Environments

Lecture 1: The Course Overview

Lecture 2: Install RStudio

Lecture 3: Install Python

Lecture 4: Launch Jupyter Notebook

Lecture 5: Learning Type Distinctions

Lecture 6: Get Started with Reinforcement Learning

Lecture 7: Real-world Reinforcement Learning Examples

Lecture 8: Key Terms in Reinforcement Learning

Lecture 9: OpenAI Gym

Lecture 10: Monte Carlo Method

Lecture 11: Monte Carlo Method in Python

Lecture 12: Monte Carlo Method in R

Lecture 13: Practical Reinforcement Learning in OpenAI Gym

Lecture 14: Markov Decision Process Concepts

Lecture 15: Python MDP Toolbox

Lecture 16: Value and Policy Iteration in Python

Lecture 17: MDP Toolbox in R

Lecture 18: Value Iteration and Policy Iteration in R

Lecture 19: Temporal Difference Learning

Lecture 20: Temporal Difference Learning in Python

Lecture 21: Temporal Difference Learning in R

Chapter 2: Advanced Practical Reinforcement Learning

Lecture 1: The Course Overview

Lecture 2: Introduction to Deep Reinforcement Learning

Lecture 3: Deep Q-Learning and Double Deep Q-Learning

Lecture 4: Q-Learning in Python

Lecture 5: Q-Learning in R

Lecture 6: TensorFlow

Lecture 7: TensorFlow in Python

Lecture 8: Deep Q-Learning with TensorFlow in Python

Lecture 9: Keras

Lecture 10: Keras in Python

Lecture 11: Deep Q-Learning with Keras in Python

Lecture 12: Deep Q-Learning with Keras in R

Lecture 13: Case Study – Reinforcement Learning

Chapter 3: Hands-On Deep Q-Learning

Lecture 1: The Course Overview

Lecture 2: Artificial Intelligence in a Nutshell

Lecture 3: Reinforcement Learning Dynamics

Lecture 4: The Bellman Equation

Lecture 5: Markov Decision Process

Lecture 6: Policy versus Plan and Living Penalty

Lecture 7: Q-Learning Intuition

Lecture 8: Temporal Difference

Lecture 9: Learning Phase of Deep Q-Learning

Lecture 10: Acting Phase of Deep Q-Learning

Lecture 11: Experience Reply and Action Selection Policies

Lecture 12: Installing PYTORCH environment

Lecture 13: Self Driving Car – Part 1

Lecture 14: Self Driving Car – Part 2

Lecture 15: Self Driving Car – Part 3

Lecture 16: Playing with Our SDC AI

Lecture 17: Convolutional Neural Network

Lecture 18: Deep Convolutional Q-Learning

Lecture 19: Eligibility Trace

Lecture 20: Installing OpenAIGym and ppaquette

Lecture 21: Build an AI for DOOM – Part 1

Lecture 22: Build an AI for DOOM – Part 2

Lecture 23: Build an AI for DOOM – Part 3

Lecture 24: Playing with our AI in DOOM

Chapter 4: Reinforcement Learning with TensorFlow & TRFL

Lecture 1: The Course Overview

Lecture 2: Set Up and Installation

Lecture 3: Getting Started with TD Learning

Lecture 4: Exploiting Off-policy Efficiency Using Q Learning

Lecture 5: Comparing On-policy Methods with SARSA and SARSE

Lecture 6: Implementing a Deep Q Network and Applying Target Network Updates

Lecture 7: Modifying a DQN with Double DQN, Persistent DQN, and Huber Loss

Lecture 8: Improving a DQN with Distributional Q Learning

Lecture 9: Utilizing Policy Gradient Methods

Lecture 10: Increasing Exploration with Policy Entropy Loss

Lecture 11: Applying Actor-Critic with A3C and A2C

Lecture 12: Performing Deterministic Policy Gradients

Lecture 13: Deploying TD(λ)

Lecture 14: Balancing Bias and Variance with Generalized λ Returns

Lecture 15: Applying Q(λ)

Lecture 16: Working with Multi-step Forward View

Lecture 17: Using Importance Sampling with Retrace (λ)

Lecture 18: Getting Started with Impala with V-Trace

Lecture 19: Augmenting an Agent with Unreal and Pixel Control

Instructors

Rating Distribution

  • 1 stars: 3 votes
  • 2 stars: 2 votes
  • 3 stars: 4 votes
  • 4 stars: 2 votes
  • 5 stars: 2 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

You may also like

Top 10 Ethical AI Courses to Learn in November 2024

Top 10 Game Development Courses to Learn in November 2024

Top 10 Software Testing Courses to Learn in November 2024