Deep Reinforcement Learning Specialist Certificate

1.Learning Methodology

• Instructor-Led Classroom Training (ILT).

2.Prerequisites:

• Basic skills with at least one programming language are desirable
• Familiar with the basic math and statistic concepts
• Deep Learning Specialist Certificate

3.Training Program Description:

• In this program, you’ll learn the theory and practice driving recent advances in
  deep reinforcement learning. This program will cover the latest techniques used to create artificially intelligent agents that can solve a variety of complex tasks, with applications ranging from gaming to finance to robotics. With the practical skills you gain in this program, you’ll be able to understand the most cutting-edge research papers, and build an impressive portfolio containing your own coding implementations.
• This program is designed to enhance your existing machine learning and deep learning skills with the addition of reinforcement learning theory and programming techniques, it will grow your deep learning and reinforcement learning expertise and give you the skills you need to understand the most recent advancements in deep reinforcement learning and build and implement your own algorithms.

• Duration of Program: 5 weeks

4.Projects

•  This program is comprised of many career-oriented projects. Each project you build will be an opportunity to demonstrate what you’ve learned in the lessons. Your completed projects will become part of a career portfolio that will demonstrate to potential employers that you have skills in data analysis and feature engineering, machine learning algorithms, and training and evaluating models.
• One of our main goals at EAII is to help you create a job-ready portfolio of completed projects. Building a project is one of the best ways to test the skills you’ve acquired and to demonstrate your newfound abilities to future employers or colleagues. Throughout this program, you’ll have the opportunity to prove your skills by building the following projects
•  Building a project is one of the best ways both to test the skills you’ve acquired and to demonstrate your newfound abilities to future employers. Throughout this program, you’ll have the opportunity to prove your skills by building the following projects:

• Project 1: Navigation
• Project 2: Continuous Control
• Project 3: Collaboration and Competition
• Project 4: Teach a Quadcopter to Fly
• Capstone Project

5.Training Program Curriculum:

I-Deep Reinforcement Topics

• Foundations of Reinforcement Learning
  • INTRODUCTION TO RL
  • The RL FRAMEWORK: THE PROBLEM
  • THE RL FRAMEWORK: THE SOLUTION
  • DYNAMIC PROGRAMMING
  • MONTE CARLO METHODS
  • TEMPORAL-DIFFERENCE METHODS
  • SOLVE OPEN-AI GYM’S TAXI-V2 TASK
  • RL IN CONTINUOUS SPACES

• Value-Based Methods
  • DEEP LEARNING IN PyTorch
  • DEEP Q-LEARNING
  • DEEP RL FOR ROBOTICS

• Policy-Based Methods
  • INTRODUCTION TO POLICY-BASED METHODS
  • IMPROVING POLICY GRADIENT METHODS
  • ACTOR-CRITIC METHODS
  • DEEP RL FOR FINANCIAL TRADING

• Multi-Agent Reinforcement Learning
  • INTRODUCTION TO MULTI-AGENT RL
  • CASE STUDY: ALPHAZERO