Getting Started with Reinforcement Learning bundle 
Reinforcement Learning systems improve as they interact from their environment. Learn how to use this amazing technique to build impressive DL applications.
Save $69.98 on these three items:-
Reinforcement Learning in Motion
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Deep Reinforcement Learning in Action
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Grokking Deep Reinforcement Learning
Reinforcement Learning in Motion 
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 interactive, 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!
Deep Reinforcement Learning in Action
Deep Reinforcement Learning in Action teaches you how to program AI agents that adapt and improve based on direct feedback from their environment. In this example-rich tutorial, you’ll master foundational and advanced DRL techniques by taking on interesting challenges like navigating a maze and playing video games. Along the way, you’ll work with core algorithms, including deep Q-networks and policy gradients, along with industry-standard tools like PyTorch and OpenAI Gym.
Grokking Deep Reinforcement Learning
Grokking Deep Reinforcement Learning uses engaging exercises to teach you how to build deep learning systems. This book combines annotated Python code with intuitive explanations to explore DRL techniques. You'll see how algorithms function and learn to develop your own DRL agents using evaluative feedback.
