Reinforcement Learning

reinforcement-learning-walkthrough (1)
12
Jul
$920.00

In this course, you will be introduced to Reinforcement Learning, an area of Machine Learning. You will learn the Markov Decision Processes, Bandit Algorithms, Dynamic Programming, and Temporal Difference (TD) methods. You will be introduced to Value function, Bellman Equation, and Value iteration. You will also learn Policy Gradient methods. You will learn to make decisions in uncertain environment.

Reinforcement Curriculum

Introduction to Reinforcement Learning
Learning Objectives: The aim of this module is to introduce you to the fundamentals of Reinforcement Learning and its elements. This module also introduces you to OpenAI Gym - a programming environment used for implementing RL agents. 

Topics:

  • Branches of Machine Learning
  • What is Reinforcement Learning?
  • The Reinforcement Learning Process
  • Elements of Reinforcement Learning
  • RL Agent Taxonomy
  • Reinforcement Learning Problem
  • Introduction to OpenAI Gym
Bandit Algorithms and Markov Decision Process
Learning Objectives: The aim of this module is to learn Bandit Algorithms and Markov Decision Process. 

Topics:

  • Bandit Algorithms
  • Markov Process
  • Markov Reward Process
  • Markov Decision Process
Dynamic Programming & Temporal Difference Methods

Learning Objectives: The aim of this module is to develop an understanding of Dynamic Programming Algorithms and Temporal Difference Learning methods. 

Topics:

  • Introduction to Dynamic Programming
  • Dynamic Programming Algorithms
  • Monte Carlo Methods
  • Temporal Difference Learning Methods
Deep Q Learning
Learning Objectives: The aim of this module is to learn Policy Gradients and develop an understanding of Deep Q Learning 

Topics:

  • Policy Gradients
  • Policy Gradients using TensorFlow
  • Deep Q learning
  • Q learning with replay buffers, target networks, and CNN
In-class Project

Goal:

     The aim of this module is to provide you hands-on experience in Reinforcement Learning.

 

Reinforcement Description

About Reinforcement Learning Course
    In this course, you will be introduced to Reinforcement Learning, an area of Machine Learning. You will learn the Markov Decision Processes, Bandit Algorithms, Dynamic Programming, and Temporal Difference (TD) methods. You will be introduced to Value function, Bellman Equation, and Value iteration. You will also learn Policy Gradient methods. You will learn to make decisions in uncertain environment.

 

Who should go for this training?
  • Web Developers
  • Software Developers
  • Programmers
  • Anyone who wants to learn reinforcement learning
What are the pre-requisites for this Course?
Required Pre-requisites
  • Fundamentals in AI & ML, Probability, Python, Neural Networks, Frameworks, Deep Learning library like PyTorch/ Theano/ Tensorflow
CoursesIT offers you complimentary self-paced courses
  • Statistics and Machine learning algorithms
  • Python Essentials
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Course Content

Time: 10 weeks

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$920.00
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