Advanced Reinforcement Learning
offered by MIT USA

Private University
168 acre campus
Estd. 1861

Advanced Reinforcement Learning
at
MIT USA
Overview

Explore the cutting-edge of RL research, and enhance the ability to identify the correct approach for applying advanced frameworks to pressing industry challenges

Duration	2 days
Total fee	₹2.07 Lakh
Mode of learning	Online
Schedule type	Self paced
Official Website	Go to Website

Advanced Reinforcement Learning

Table of content

Overview
Highlights
Course Details
Curriculum
Faculty
Entry Requirements

Advanced Reinforcement Learning
at
MIT USA
Highlights

Earn a certificate after completion of the course
Learn from industry experts

Advanced Reinforcement Learning
at
MIT USA
Course details

Who should do this course?

For Research scientists
For Data scientists
For Data analysts and business analysts
For Machine learning engineers and software engineers
For Product managers and program managers
For CTOs and other technology leaders

What are the course deliverables?

Determine the reinforcement learning framework (e.g. goal-directed, hierarchical, offline reinforcement learning, bandits) that is best-suited to solve a specific problem
Select the most promising algorithms for an already-formulated reinforcement learning problem
Recognize the limitations of reinforcement learning in order to judge whether a situation is suited for these strategies

More about this course

This course is tailored for individuals seeking an in-depth exploration of cutting-edge concepts and applications in the field of reinforcement learning (RL)
In this course participants will gain insights into how advanced RL methods are reshaping industries and solving complex decision-making problems

Advanced Reinforcement Learning
at
MIT USA
Curriculum

Exploration in Complex Environments

Multi-Agent Reinforcement Learning (MARL)

Deep Reinforcement Learning (DRL)

Hierarchical Reinforcement Learning

Meta-Reinforcement Learning

Off-Policy Reinforcement Learning

Advanced Reinforcement Learning
at
MIT USA
Faculty details

Pulkit Agrawal

Pulkit Agrawal is assistant professor of electrical engineering and computer science at MIT and leads the Improbable AI Lab, part of the Computer Science and Artificial Intelligence Lab at MIT and affiliated with the Laboratory for Information and Decision Systems.

Cathy Wu

Cathy Wu is the Gilbert W. Winslow Career Development Assistant Professor of civil and environmental engineering at MIT and has worked across many fields and organizations, including Microsoft Research, OpenAI, the Google X Self-Driving Car Team, AT&T, Caltrans, Facebook, and Dropbox.