Course curriculum

  • 1

    Introduction

    • Introduction to Instructor
    • Introduction to Course
  • 2

    Motivation & Applications

    • What Is Reinforcement Learning
    • WhatIs Reinforcement Learning hiders and seekers by OpenAI
    • RL vs Other ML Frameworks
    • Why RL
    • Examples Of RL
    • Limitations Of RL
    • Exercises
  • 3

    Terminologies of RL

    • Introduction
    • Envionment
    • Agent
    • Action
    • State
    • Goal and Done State
    • Reward
    • Fun Activity
    • Policy and Plan
    • Episode
  • 4

    Naïve Random Solution

    • Introduction to Module
    • Introduction to Game
    • Rules of Game
    • Setting up game Python pt 1
    • Setting up game Python pt 2
    • Setting up game Python pt 3
    • Playing the game manually
    • Implementing Random solution
    • Q Learning and Q Table Theory
    • Implemeting Q Learning pt 1
    • Dry Run of get state
    • Answer to Question
    • Implemeting Q Learning pt 2
    • Implemeting Q Learning pt 3
    • Conclusion
  • 5

    RL based Q Learning Solution

    • Introduction to Gym
    • Frozen Lake Rules
    • Implementing Frozen Lake pt 1
    • Implementing Frozen Lake pt 2
    • Implementing Frozen Lake pt 3
    • Implementing Frozen Lake pt 4
    • Agent plays the game
    • Conclusion
  • 6

    Hyper Parameters & Concepts

    • Introduction to Module
    • Epsilon
    • Updating Epsilon Value
    • Gamma, Discount Factor
    • Alpha Learning Rate
    • Q Learning Equation
    • Quiz (Number of Episodes)
    • Solution (Number of Episodes)
    • Quiz (Alpha)
    • Solution (Alpha)
  • 7

    SARSA

    • Introduction to SARSA
    • Off policy VS On policy
    • SARSA Implementation
    • SARSA Implementation update
    • Pros & Cons
  • 8

    DNN Foundation for Deep RL

    • Why Deep Learning
    • Why PyTorch
    • PyTorch installation and Tensors intro
    • Automatic Diffrenciation Pytorch New
    • Why DNNs in Machine Learning
    • Representational Power and Data Utilization Capacity of DNN
    • Perceptron
    • Perceptron Exercise
    • Perceptron Exercise Solution
    • Perceptron Implementation
    • DNN Architecture
    • DNN Architecture Exercise
    • DNN Architecture Exercise Solution
    • DNN ForwardStep Implementation
    • DNN Why Activation Function is Required
    • DNN Why Activation Function is Required Exercise
    • DNN Why Activation Function is Required Exercise Solution
    • MDP
    • DNN Properties of Activation Function
    • DNN Activation Functions in Pytorch
    • DNN What is Loss Function
    • DNN What is Loss Function Exercise
    • DNN What is Loss Function Exercise Solution
    • DNN What is Loss Function Exercise 2
    • DNN What is Loss Function Exercise 2 Solution
    • DNN Loss Function in Pytorch
    • DNN Gradient Descent
    • DNN Gradient Descent Exercise
    • DNN Gradient Descent Exercise Solution
    • DNN Gradient Descent Implementation
    • DNN Gradient Descent Stochastic Batch Minibatch
    • DNN Gradient Descent Summary
    • DNN Implemenation Gradient Step
    • DNN Implemenation Stochastic Gradient Descent
    • DNN Implemenation Batch Gradient Descent
    • DNN Implemenation Minibatch Gradient Descent
    • DNN Implemenation in PyTorch
    • DNN Weights Initializations
    • DNN Learning Rate
    • DNN Batch Normalization
    • DNN Batch Normalization Implementation
    • DNN Optimizations
    • DNN Dropout
    • DNN Dropout in PyTorch
    • DNN Early Stopping
    • DNN Hyperparameters
    • DNN Pytorch CIFAR10 Example
  • 9

    Deep RL DQN

    • Introduction & Recap
    • DQN Algorithm Steps
    • Introduction to Project (Cart pole)
    • Policy Network Explained
    • Neural Network Class Implementation
    • Replay Memory & Experience
    • Experience Implementation
    • Replay Memory Implementatiton
    • Target Network & Recap
    • Epsilon Greeady Strategy Implemented
    • Agent Class Implemented
    • Environment Manager Implementation
    • How to Get State
    • Screen Preprocessing
    • Screen Croping
    • Screen Transformation
    • Processed VS NonProcessed Screen
    • Moving Avg Implemented
    • Ploting the Moving Avg
    • Hyperparameter Initialization
    • Initializing the Classes
    • Final Structure Implementation part 1
    • Extracting Tensors
    • Final Structure Implementation part 2
    • Qvalues Calculator Implemented
    • Removing Errors Final Structure Implementation part 3
    • Visualizing the Training
  • 10

    StableBaseLines Cartpole Solution

    • Introduction to Stable Baseline
    • Loading & Understanding the Envireonment
    • Train RL Model
    • Evaluation and Testing
    • Callbacks & Early Stopping
    • Changing Policy Architecture
    • Changing the Algorithm
    • Tips for Accuracy Improvement
  • 11

    Trading Bot RL

    • Introduction to Libraries and Project
    • Loading the Data
    • Setting up Environment
    • Random Actions
    • Training and Evaluating Model
  • 12

    Car Racing Game

    • Introduction to Game
    • Importing the Dependencies
    • Exploring the Environment
    • Training and Testing the Model
  • 13

    Interview Prep

    • Prep 1
    • Prep 2