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Book Overview & Buying
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Table Of Contents
Hands-On Artificial Intelligence for IoT - Second Edition
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Reinforcement Learning (RL) is very different from both supervised and unsupervised learning. It’s the way most living beings learn—interacting with the environment. In this chapter, we’ll study different algorithms that are employed for RL. You’ll learn what RL is, see how it’s different from supervised learning and unsupervised learning, understand the various elements of RL, and explore some fascinating applications of RL in the real world. Additionally, you’ll gain an understanding of the OpenAI interface for training RL agents, delve into Q-learning and use it to train an RL agent, and discover Deep Q-Networks (DQNs) to train an agent to play Atari. You’ll also learn about the policy gradient algorithm, use stable baselines to train an RL agent, and finally, train an RL agent to walk using Deep Deterministic Policy Gradient (DDPG).
As you progress through the chapter, you’ll learn about the...