- Course
Modified Reinforcement Learning
Learn how to adapt reinforcement learning (RL) algorithms to constrained, hierarchical, and real-world tasks. This course will teach you to implement modified RL techniques, optimize agent behavior, and evaluate performance in practical applications.
Intermediate
- Course
Modified Reinforcement Learning
Learn how to adapt reinforcement learning (RL) algorithms to constrained, hierarchical, and real-world tasks. This course will teach you to implement modified RL techniques, optimize agent behavior, and evaluate performance in practical applications.
Intermediate
Get started today
Access this course and other top-rated tech content with one of our business plans.
Try this course for free
Access this course and other top-rated tech content with one of our individual plans.
This course is included in the libraries shown below:
- AI
What you'll learn
Reinforcement learning (RL) often assumes idealized environments, but real-world tasks come with constraints and unique objectives.
In this course, Modified Reinforcement Learning, you’ll learn to adapt RL algorithms to meet such challenges.
First, you’ll explore the theory behind modified RL and how reward shaping, inverse RL, and constraints guide agent behavior.
Next, you’ll discover practical strategies for adapting Q-learning and policy gradients to real-world constraints, including hierarchical and curriculum-based methods.
Finally, you’ll learn to evaluate your modified RL agents, understand trade-offs, and apply them safely in simulation.
When you are finished with this course, you’ll have the skills needed to implement and analyze Modified RL solutions in practical scenarios.
Modified Reinforcement Learning
Intermediate
Table of contents
Dr. Yasir Khan is a global tech consultant and 38Labs founder. He's passionate about digital transformation, data & AI, and regularly shares technology insights on Pluralsight.