Referral program

Reinforcement Learning Explained

Rp500,000 Rp99,000

Product price
Additional options total:
Order total:
  • Description
  • Unit Outline
  • Instructor
  • Additional information
  • Certificate
  • Reviews (0)


About this course

This course is part of the Microsoft Professional Program in Artificial Intelligence.

Reinforcement Learning (RL) is an area of machine learning, where an agent learns by interacting with its environment to achieve a goal.

In this course, you will be introduced to the world of reinforcement learning. You will learn how to frame reinforcement learning problems and start tackling classic examples like news recommendation, learning to navigate in a grid-world, and balancing a cart-pole.

You will explore the basic algorithms from multi-armed bandits, dynamic programming, TD (temporal difference) learning, and progress towards larger state space using function approximation, in particular using deep learning. You will also learn about algorithms that focus on searching the best policy with policy gradient and actor critic methods. Along the way, you will get introduced to Project Malmo, a platform for Artificial Intelligence experimentation and research built on top of the Minecraft game.

What you’ll learn

  • Reinforcement Learning Problem
  • Markov Decision Process
  • Bandits
  • Dynamic Programming
  • Temporal Difference Learning
  • Approximate Solution Methods
  • Policy Gradient and Actor Critic
  • RL that Works

Estimate Time : 24-48 hours

Module 1 Introduction to Reinforcement LEarning

  • What is Reinforcement Learning
  • Comparisons
  • Elements of RL
  • Lab

Module 2 Bandits

  • Bandits Framework
  • Regret Minimization
  • Bridge to RL
  • Lab

Module 3 Reinforcement Learning Problem

  • Agent and Environment Interface
  • Markov Decision Process
  • Lab

Module 4 Dynamic Programming

  • Basics of DP
  • DP Observations
  • Lab

Module 5 Temporal Difference Learning

  • Policy Evaluation
  • Policy Optimization
  • Lab

Module 6 Function Approximation

  • Why use Function Approximation
  • Linear Function Approximation
  • RL with Deep Neural Networks
  • Extensions to Deep Q-Learning
  • Lab

Module 7 Policy Gradient and Actor Critic

  • Introduction to Policy Optimization
  • Likelihood Ratio Methods
  • Variance Reduction
  • Actor Critic
  • Lab

Jonathan Sanito
Senior Content Developer Microsoft

Jonathan works as a content developer and project manager for Microsoft focusing in Data and Analytics online training. He has worked with trainings for developer and IT pro audiences, from Microsoft Dynamics NAV to Windows Active Directory. Before coming to Microsoft, Jonathan worked as a consultant for a Microsoft partner, implementing Microsoft Dynamics NAV solutions.

Roland Fernandez
Senior Researcher and AI School Instructor, Deep Learning Technology Center Microsoft Research AI

Roland works as a researcher and AI School instructor in the Deep Learning Technology Center of Microsoft Research AI. His interests include reinforcement learning, autonomous multitask learning, symbolic representation, AI education, information visualization, and HCI. Before coming to the DLTC, Roland worked in the VIBE group of MSR doing visualization and HCI projects, most notably the SandDance project. Before MSR, Roland worked (at Microsoft and other companies) in the areas of Natural User Interfaces, Activity Based Computing, Advanced Prototyping, Programmer Tools, Operating Systems, and Databases.

Adith Swaminathan
Researcher Microsoft Research AI

Adith is a researcher at the Deep Learning Technology Center at Microsoft Research. He studies principles and algorithms that can improve human-centered systems using machine learning. Adith spent the 2015-16 academic year visiting the Information and Language Processing Systems group at the University of Amsterdam, interned with the Machine Learning group at Microsoft Research NYC during the summer of 2015, Computer Human Interactive Learning group (now called Machine Teaching Group) at Microsoft Research Redmond during the summer of 2013, Search Labs at Microsoft Research during the summer of 2012, and worked as a strategist with Tower Research Capital for 14 months from June 2010-July 2011.

Kenneth Tran
Principal Research Engineer Microsoft Research AI

Kenneth is a Principal Research Engineer at the Deep Learning Technology Center. He has wide interest in Machine Learning spanning from optimization algorithms to distributed systems. His current main research pursuit is deep reinforcement learning with focus on off-policy learning and sample efficient methods, safe exploration, reverse reinforcement learning and real-world optimal control applications, including drones control, data center energy optimization, indoor farming optimization, etc.

Katja Hofmann
Researcher Microsoft Research AI

Katja is a researcher at the Machine Intelligence and Perception group at Microsoft Research Cambridge. She is the research lead of Project Malmo, which uses the popular game Minecraft as an experimentation platform for developing intelligent technology. Her long-term goal is to develop AI systems that learn to collaborate with people, to empower their users and help solve complex real-world problems. Outside of Project Malmo, Katja works on online evaluation and interactive learning for information retrieval, which means understanding how we can apply machine learning an artificial intelligence to develop more intelligent search and recommendation systems.

Matthew Hausknecht
Researcher Microsoft Research AI

Matthew is a researcher at Microsoft Research. His interests involve expanding the capabilities of intelligent agents. His main research is at the intersection of Reinforcement Learning and Deep Learning. Matthew received his PhD from the University of Texas at Austin under the supervision of Peter Stone.

Additional information

Author / Publisher



Beginner, Intermediate





There are no reviews yet.

Only logged in customers who have purchased this product may leave a review.

You've just added this product to the cart:

Invite & Earn

Signup to start sharing your link

Available Coupon