Reinforcement Learning Onramp

Offered By: MathWorks via MATLAB Academy

Tags

MATLAB Courses Reinforcement Learning Courses Neural Networks Courses Simulink Courses Reward Systems Courses

Course Description

Overview

Overview of Reinforcement Learning: Familiarize yourself with reinforcement learning concepts and the course.
Defining the Environment: Define how an agent interacts with an environment model.
Defining Agents: Create representations of RL agents.
Training Agents: Use simulation episodes to train an agent.
Conclusion: Learn next steps and give feedback on the course.

Syllabus

What is Reinforcement Learning
Simulating with a Pretrained Agent
Components of a Reinforcement Learning Model
Defining an Environment Interface
Providing Rewards
Including Actions in the Reward
Connecting a Simulink Environment to a MATLAB Agent
Critics and Q Values
Representing Critics for Continuous Problems
Creating Neural Networks
Creating Networks for Agents
Actors and Critics
Creating Default Agent Representations
Summary of Agents
Training
Changing Options
Improving Training
Summary of Functions
Additional Resources
Survey

Taught by

Matt Tearle

Related Courses

Inspiring and Motivating Individuals
University of Michigan via Coursera Managing Social and Human Capital
University of Pennsylvania via Coursera Inspiring and Motivating Arts and Culture Teams
University of Michigan via edX Management Fundamentals
University of Pennsylvania via Coursera Strategic Performance Management
Indian Institute of Technology, Kharagpur via Swayam