{"product_id":"reinforcement-learning-in-robotics-training-autonomous-agents-to-navigate-complex-physical-environments-9798247851165","title":"Reinforcement Learning in Robotics: Training autonomous agents to navigate complex physical environments","description":"\u003cp\u003e • Author(s): Nathan Westwood\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Robotics\u003c\/p\u003e\u003cp\u003e\u003cb\u003eStop Programming Robots. Teach Them to Learn.\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eHard-coding every movement is impossible. The real world is too chaotic.\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eTraditional robotics relies on rigid \"If\/Then\" logic. But what happens when the robot encounters something it hasn't been programmed for? It fails. \u003ci\u003eReinforcement Learning in Robotics\u003c\/i\u003e is the guide to building the next generation of adaptive machines-robots that learn to walk, grasp, and navigate by trial and error, just like a child.\u003c\/p\u003e\u003cp\u003eThis book bridges the gap between the theoretical math of Reinforcement Learning (RL) and the physical reality of hardware. You will move from simple grid worlds to complex physics simulations, training agents that discover optimal strategies on their own.\u003c\/p\u003e\u003cb\u003eFrom Simulation to Reality (Sim2Real)\u003c\/b\u003e\u003cp\u003eThis is a hands-on guide to the algorithms driving modern robotics research.\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eThe RL Loop: \u003c\/b\u003e Master the fundamental cycle of \u003cb\u003eAgent, Environment, State, Action, and Reward\u003c\/b\u003e. Understand how to design \"Reward Functions\" that encourage the behavior you want without \"gaming the system.\"\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eDeep Q-Networks (DQN): \u003c\/b\u003e Learn how deep neural networks can approximate the value of actions in complex, high-dimensional spaces.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003ePolicy Gradients (PPO \u0026amp; SAC): \u003c\/b\u003e Dive into the state-of-the-art algorithms used by Boston Dynamics and OpenAI to train robots for continuous control tasks like walking or flying.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eSimulation Environments: \u003c\/b\u003e Learn to use \u003cb\u003ePyBullet\u003c\/b\u003e or \u003cb\u003eGazebo\u003c\/b\u003e to train your robot safely in a virtual world before deploying the \"Brain\" to real hardware.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eThe Reality Gap: \u003c\/b\u003e Crucial techniques for \"Domain Randomization\" to ensure that what your robot learns in the simulator actually works in the messy real world.\u003c\/p\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eWhether you are a researcher trying to solve the \"grasping problem,\" or an engineer building a drone that can dodge obstacles, this book provides the mathematical and practical framework to make it happen.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eDon't write the rules. Let the robot discover them. Scroll up and grab your copy to master the future of autonomous control.\u003c\/b\u003e\u003c\/p\u003e","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":47569892212887,"sku":"9798247851165","price":1421.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798247851165.webp?v=1774879527","url":"https:\/\/atlanticbooks.com\/products\/reinforcement-learning-in-robotics-training-autonomous-agents-to-navigate-complex-physical-environments-9798247851165","provider":"Atlantic Books","version":"1.0","type":"link"}