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.

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.