Reinforcement Learning in Motion introduces you to the exciting world of machine systems that learn from their environments! Developer, data scientist, and expert instructor Phil Tabor guides you from the basics all the way to programming your own constantly-learning AI agents. In this course, he’ll break down key concepts like how RL systems learn, how to sense and process environmental data, and how to build and train AI agents. As you learn, you’ll master the core algorithms and get to grips with tools like Open AI Gym, numpy, and Matplotlib.
Reinforcement systems learn by doing, and so will you in this hands-on course! You’ll build and train a variety of algorithms as you go, each with a specific purpose in mind. The rich and interesting examples include simulations that train a robot to escape a maze, help a mountain car get up a steep hill, and balance a pole on a sliding cart. You’ll even teach your agents how to navigate Windy Gridworld, a standard exercise for finding the optimal path even with special conditions!
With reinforcement learning, an AI agent learns from its environment, constantly responding to the feedback it gets. The agent optimizes its behavior to avoid negative consequences and enhance positive outcomes. The resulting algorithms are always looking for the most positive and efficient outcomes!
Importantly, with reinforcement learning you don’t need a mountain of data to get started. You just let your AI agent poke and prod its environment, which makes it much easier to take on novel research projects without well-defined training datasets.