Python Reinforcement Learning, Deep Q-Learning And Trfl

Leverage the power of Reinforcement Learning techniques to develop intelligent systems using Python

What you'll 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

Requirements

Basic knowledge of Python is required.

Description

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.

Overview

Section 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

Section 2: Advanced Practical Reinforcement Learning

Lecture 22 The Course Overview

Lecture 23 Introduction to Deep Reinforcement Learning

Lecture 24 Deep Q-Learning and Double Deep Q-Learning

Lecture 25 Q-Learning in Python

Lecture 26 Q-Learning in R

Lecture 27 TensorFlow

Lecture 28 TensorFlow in Python

Lecture 29 Deep Q-Learning with TensorFlow in Python

Lecture 30 Keras

Lecture 31 Keras in Python

Lecture 32 Deep Q-Learning with Keras in Python

Lecture 33 Deep Q-Learning with Keras in R

Lecture 34 Case Study – Reinforcement Learning

Section 3: Hands-On Deep Q-Learning

Lecture 35 The Course Overview

Lecture 36 Artificial Intelligence in a Nutshell

Lecture 37 Reinforcement Learning Dynamics

Lecture 38 The Bellman Equation

Lecture 39 Markov Decision Process

Lecture 40 Policy versus Plan and Living Penalty

Lecture 41 Q-Learning Intuition

Lecture 42 Temporal Difference

Lecture 43 Learning Phase of Deep Q-Learning

Lecture 44 Acting Phase of Deep Q-Learning

Lecture 45 Experience Reply and Action Selection Policies

Lecture 46 Installing PYTORCH environment

Lecture 47 Self Driving Car – Part 1

Lecture 48 Self Driving Car – Part 2

Lecture 49 Self Driving Car – Part 3

Lecture 50 Playing with Our SDC AI

Lecture 51 Convolutional Neural Network

Lecture 52 Deep Convolutional Q-Learning

Lecture 53 Eligibility Trace

Lecture 54 Installing OpenAIGym and ppaquette

Lecture 55 Build an AI for DOOM – Part 1

Lecture 56 Build an AI for DOOM – Part 2

Lecture 57 Build an AI for DOOM – Part 3

Lecture 58 Playing with our AI in DOOM

Section 4: Reinforcement Learning with TensorFlow & TRFL

Lecture 59 The Course Overview

Lecture 60 Set Up and Installation

Lecture 61 Getting Started with TD Learning

Lecture 62 Exploiting Off-policy Efficiency Using Q Learning

Lecture 63 Comparing On-policy Methods with SARSA and SARSE

Lecture 64 Implementing a Deep Q Network and Applying Target Network Updates

Lecture 65 Modifying a DQN with Double DQN, Persistent DQN, and Huber Loss

Lecture 66 Improving a DQN with Distributional Q Learning

Lecture 67 Utilizing Policy Gradient Methods

Lecture 68 Increasing Exploration with Policy Entropy Loss

Lecture 69 Applying Actor-Critic with A3C and A2C

Lecture 70 Performing Deterministic Policy Gradients

Lecture 71 Deploying TD(λ)

Lecture 72 Balancing Bias and Variance with Generalized λ Returns

Lecture 73 Applying Q(λ)

Lecture 74 Working with Multi-step Forward View

Lecture 75 Using Importance Sampling with Retrace (λ)

Lecture 76 Getting Started with Impala with V-Trace

Lecture 77 Augmenting an Agent with Unreal and Pixel Control

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.