• Online, Self-Paced
Course Description

Explore machine learning reinforcement learning, along with the essential components of reinforcement learning that will assist in the development of critical algorithms for decision making, in this 10-video course. You will examine how to achieve continuous improvement in performance of machines or programs over time, along with key differences between reinforcement learning and machine learning paradigm. Learners will observe how to depict the flow of reinforcement learning by using agent, action, and environment. Next, you will examine different scenarios of state changes and transition processes applied in reinforcement learning. Then examine the reward hypothesis, and learn to recognize the role of rewards in reinforcement learning. You will learn that all goals can be described by maximization of the expected cumulative rewards. Continue by learning the essential steps applied by agents in reinforcement learning to make decisions. You will explore the types of reinforcement learning environments, including deterministic, observable, discrete or continuous, and single-agent or multi-agent. Finally, you will learn how to install OpenAI Gym and OpenAl Universe.

Learning Objectives

Explore machine learning reinforcement learning, along with the essential components of reinforcement learning that will assist in the development of critical algorithms for decision making, in this 10-video course. You will examine how to achieve continuous improvement in performance of machines or programs over time, along with key differences between reinforcement learning and machine learning paradigm. Learners will observe how to depict the flow of reinforcement learning by using agent, action, and environment. Next, you will examine different scenarios of state changes and transition processes applied in reinforcement learning. Then examine the reward hypothesis, and learn to recognize the role of rewards in reinforcement learning. You will learn that all goals can be described by maximization of the expected cumulative rewards. Continue by learning the essential steps applied by agents in reinforcement learning to make decisions. You will explore the types of reinforcement learning environments, including deterministic, observable, discrete or continuous, and single-agent or multi-agent. Finally, you will learn how to install OpenAI Gym and OpenAl Universe.

Framework Connections

The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):

Specialty Areas

  • Systems Architecture