Reinforcement Learning And Deep Rl Python (Theory And Projects)

A comprehensive, hands-on, and easy-to-understand course on reinforcement learning. Learn about deep Q-Learning, SARSA, deep RL, car racing and trading projects, and be prepared with interview questions.

What you'll learn
Description
A comprehensive, hands-on, and easy-to-understand course on reinforcement learning. Learn about deep Q-Learning, SARSA, deep RL, car racing and trading projects, and be prepared with interview questions.About This VideoLearn from a comprehensive yet self-explanatory course, divided into 145+ videos along with detailed code notebooksStructured course with solid basic understanding and advanced practical conceptsUp-to-date, practical explanations and live coding with Python to build six projects at an adequate paceIn DetailReinforcement learning is a subset of machine learning. In the RL training method, desired actions are rewarded, and undesired actions are punished. Deep RL is also a subfield of machine learning. In deep RL, intelligent machines and software are trained to learn from their actions in the same way that humans learn from experience. Deep RL has the capability to solve complex problems that were unmanageable by machines in the past. Therefore, the potential applications of deep RL in various sectors are enormous.We will start with an introduction to reinforcement learning and look at some case studies and real-world examples. Then you will look at Naïve/Random solutions and RL-based solutions. Next, you will see different types of RL solutions such as hyperparameters, Markov Decision Process, Q-Learning, and SARSA followed by a mini project on Frozen Lake. You will then learn deep learning/neural networks and deep RL/deep Q networks. Next, you will work on car racing and trading projects. Finally, you will go through some interview questions.By the end of this course, you will be able to relate the concepts and practical applications of reinforcement and deep reinforcement learning with real-world problems and implement any project that requires reinforcement and deep reinforcement learning knowledge from scratch.AudienceThis course is designed for beginners who know absolutely nothing about reinforcement and deep reinforcement learning, the ones who want to develop intelligent solutions, and the ones who want to learn the theoretical concepts first before implementing them using Python. An individual who wants to learn PySpark along with its implementation in realistic projects, machine learning or deep learning lovers, and anyone interested in artificial intelligence will be highly benefitted.You would need prior knowledge of Python, an elementary understanding of programming, and a willingness to learn and practice.

Overview

Chapter 1 : Introduction to the Course
Lecture 1 Introduction to Instructor
Lecture 2 Course Introduction
Chapter 2 : Motivation and Applications
Lecture 1 What Is Reinforcement Learning
Lecture 2 What Is Reinforcement Learning Hiders and Seekers by OpenAI
Lecture 3 RL Versus Other ML Frameworks
Lecture 4 Why RL
Lecture 5 Examples of RL
Lecture 6 Limitations of RL
Lecture 7 Exercises
Chapter 3 : Terminologies of RL
Lecture 1 Introduction
Lecture 2 Environment
Lecture 3 Agent
Lecture 4 Action
Lecture 5 State
Lecture 6 Goal and Done State
Lecture 7 Reward
Lecture 8 Fun Activity
Lecture 9 Policy and Plan
Lecture 10 Episode
Chapter 4 : Naive Random Solution
Lecture 1 Introduction to Module
Lecture 2 Introduction to Game
Lecture 3 Rules of Game
Lecture 4 Setting Up Game in Python - 1
Lecture 5 Setting Up Game in Python - 2
Lecture 6 Setting Up Game in Python - 3
Lecture 7 Playing the Game Manually
Lecture 8 Implementing Random Solution
Lecture 9 Q-Learning and Q-Table Theory
Lecture 10 Implementing Q-Learning - 1
Lecture 11 Dry Run of Get State
Lecture 12 Implementing Q Learning - 2
Lecture 13 Implementing Q Learning - 3
Lecture 14 Conclusion
Chapter 5 : RL-Based Q-Learning
Lecture 1 Introduction to Gym
Lecture 2 Frozen Lake Rules
Lecture 3 Implementing Frozen Lake - 1
Lecture 4 Implementing Frozen Lake - 2
Lecture 5 Implementing Frozen Lake - 3
Lecture 6 Implementing Frozen Lake - 4
Lecture 7 Agent Plays the Game
Lecture 8 Conclusion
Chapter 6 : Hyper Parameters and Concepts
Lecture 1 Introduction to Module
Lecture 2 Epsilon
Lecture 3 Updating Epsilon Value
Lecture 4 Gamma and Discount Factor
Lecture 5 Alpha Learning Rate
Lecture 6 Q-Learning Equation
Lecture 7 Quiz (Number of Episodes)
Lecture 8 Solution (Number of Episodes)
Lecture 9 Quiz (Alpha)
Lecture 10 Solution (Alpha)
Chapter 7 : SARSA (State–Action–Reward–State–Action)
Lecture 1 Introduction to SARSA
Lecture 2 Off Policy Versus On Policy
Lecture 3 SARSA Implementation
Lecture 4 SARSA Implementation update
Lecture 5 Pros and Cons
Chapter 8 : DNN Foundation for Deep RL
Lecture 1 Why Deep Learning
Lecture 2 Why PyTorch
Lecture 3 PyTorch Installation and Tensors Introduction
Lecture 4 Automatic Differentiation PyTorch
Lecture 5 Why DNNs in Machine Learning
Lecture 6 Representational Power and Data Utilization Capacity of DNN
Lecture 7 Perceptron
Lecture 8 Perceptron Exercise
Lecture 9 Perceptron Exercise Solution
Lecture 10 Perceptron Implementation
Lecture 11 DNN Architecture
Lecture 12 DNN Architecture Exercise
Lecture 13 DNN Architecture Exercise Solution
Lecture 14 DNN ForwardStep Implementation
Lecture 15 DNN Why Activation Function Is Required
Lecture 16 DNN Why Activation Function Is Required Exercise
Lecture 17 DNN Why Activation Function Is Required Exercise Solution
Lecture 18 MDP (Markov Decision Process)
Lecture 19 DNN Properties of Activation Function
Lecture 20 DNN Activation Functions in PyTorch
Lecture 21 DNN What Is Loss Function
Lecture 22 DNN What Is Loss Function Exercise
Lecture 23 DNN What Is Loss Function Exercise Solution
Lecture 24 DNN What Is Loss Function Exercise - 2
Lecture 25 DNN What Is Loss Function Exercise Solution - 2
Lecture 26 DNN Loss Function in PyTorch
Lecture 27 DNN Gradient Descent
Lecture 28 DNN Gradient Descent Exercise
Lecture 29 DNN Gradient Descent Exercise Solution
Lecture 30 DNN Gradient Descent Implementation
Lecture 31 DNN Gradient Descent Stochastic Batch Minibatch
Lecture 32 DNN Gradient Descent Summary
Lecture 33 DNN Implementation Gradient Step
Lecture 34 DNN Implementation Stochastic Gradient Descent
Lecture 35 DNN Implementation Batch Gradient Descent
Lecture 36 DNN Implementation Minibatch Gradient Descent
Lecture 37 DNN Implementation in PyTorch
Lecture 38 DNN Weights Initializations
Lecture 39 DNN Learning Rate
Lecture 40 DNN Batch Normalization
Lecture 41 DNN Batch Normalization Implementation
Lecture 42 DNN Optimizations
Lecture 43 DNN Dropout
Lecture 44 DNN Dropout in PyTorch
Lecture 45 DNN Early Stopping
Lecture 46 DNN Hyperparameters
Lecture 47 DNN PyTorch CIFAR10 Example
Chapter 9 : Deep RL DQN
Lecture 1 Introduction and Recap
Lecture 2 DQN Algorithm Steps
Lecture 3 Introduction to Project (Cart pole)
Lecture 4 Policy Network Explained
Lecture 5 Neural Network Class Implementation
Lecture 6 Replay Memory and Experience
Lecture 7 Experience Implementation
Lecture 8 Replay Memory Implementation
Lecture 9 Target Network and Recap
Lecture 10 Epsilon Greedy Strategy Implemented
Lecture 11 Agent Class Implemented
Lecture 12 Environment Manager Implementation
Lecture 13 How to Get State
Lecture 14 Screen Pre-Processing
Lecture 15 Screen Cropping
Lecture 16 Screen Transformation
Lecture 17 Processed Versus Non-Processed Screen
Lecture 18 Moving Avg Implemented
Lecture 19 Plotting the Moving Avg
Lecture 20 Hyperparameter Initialization
Lecture 21 Initializing the Classes
Lecture 22 Final Structure Implementation - 1
Lecture 23 Extracting Tensors
Lecture 24 Final Structure Implementation - 2
Lecture 25 Q-Values Calculator Implemented
Lecture 26 Removing Errors Final Structure Implementation - 3
Lecture 27 Visualizing the Training
Chapter 10 : Stable Baselines Cartpole Solution
Lecture 1 Introduction to Stable Baseline
Lecture 2 Loading and Understanding the Environment
Lecture 3 Train RL Model
Lecture 4 Evaluation and Testing
Lecture 5 Callbacks and Early Stopping
Lecture 6 Changing Policy Architecture
Lecture 7 Changing the Algorithm
Lecture 8 Tips for Accuracy Improvement
Chapter 11 : Trading Bot RL
Lecture 1 Introduction to Libraries and Project
Lecture 2 Loading the Data
Lecture 3 Setting Up Environment
Lecture 4 Random Actions
Lecture 5 Training and Evaluating Model
Chapter 12 : Car Racing Game
Lecture 1 Introduction to Game
Lecture 2 Importing the Dependencies
Lecture 3 Exploring the Environment
Lecture 4 Training and Testing the Model
Chapter 13 : Interview Prep
Lecture 1 Prep 1
Lecture 2 Prep 2