{"product_id":"reinforcement-learning-with-python-for-drones-train-autonomous-quadcopters-to-hover-navigate-and-land-using-self-learning-ai-algorithms-9798189398582","title":"Reinforcement Learning with Python for Drones: Train Autonomous Quadcopters to Hover, Navigate, and Land Using Self-Learning AI Algorithms","description":"\u003cp\u003e • Author(s): Nathan Westwood\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Programming Languages - Python\u003c\/p\u003e\u003cp\u003e\u003cb\u003eTeach a simulated quadcopter to hover, navigate, avoid obstacles, and land through repeated practice.\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eA quadcopter is easy to admire but difficult to control. Every motor change affects altitude, tilt, speed, direction, and stability. Wind, sensor noise, changing weight, and timing errors make the problem even harder.\u003c\/p\u003e\u003cp\u003e\u003ci\u003eReinforcement Learning with Python for Drones\u003c\/i\u003e shows you how to train autonomous drone behaviours in simulation before moving cautiously toward real hardware.\u003c\/p\u003e\u003cp\u003eUsing Python, NumPy, Gymnasium, PyBullet, Matplotlib, and Stable-Baselines3, this beginner-friendly guide will help you: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eUnderstand how quadcopters move, hover, turn, and respond to thrust\u003c\/li\u003e\n\u003cli\u003eLearn the reinforcement-learning loop of observations, actions, rewards, steps, and episodes\u003c\/li\u003e\n\u003cli\u003eCreate a clean Python development environment for repeatable experiments\u003c\/li\u003e\n\u003cli\u003eBuild a reusable simulated drone environment with Gymnasium and PyBullet\u003c\/li\u003e\n\u003cli\u003eDesign useful observation spaces, action spaces, reward functions, and termination rules\u003c\/li\u003e\n\u003cli\u003eTrain a simple altitude-control agent with Q-learning\u003c\/li\u003e\n\u003cli\u003eMove from Q-tables to neural-network-based learning\u003c\/li\u003e\n\u003cli\u003eTrain a hovering agent with Proximal Policy Optimisation, or PPO\u003c\/li\u003e\n\u003cli\u003eImprove hovering under varied starting positions, sensor noise, wind, and mass changes\u003c\/li\u003e\n\u003cli\u003eTrain a drone to navigate toward waypoints\u003c\/li\u003e\n\u003cli\u003eTeach an agent to avoid static and random obstacles\u003c\/li\u003e\n\u003cli\u003eBuild an autonomous landing task with alignment, descent, touchdown, and failure handling\u003c\/li\u003e\n\u003cli\u003eUse domain randomisation to reduce the gap between simulation and reality\u003c\/li\u003e\n\u003cli\u003eEvaluate results using hovering error, navigation success, collision rates, and landing accuracy\u003c\/li\u003e\n\u003cli\u003eCompare DQN, PPO, and SAC for drone-control tasks\u003c\/li\u003e\n\u003cli\u003eSave models, organise logs, plot results, and make experiments reproducible\u003c\/li\u003e\n\u003cli\u003eMove cautiously through software-in-the-loop, bench testing, manual override, emergency stop, and controlled flight preparation\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eYou do not need previous robotics or reinforcement-learning experience. The book introduces the required Python concepts as they become necessary, including variables, functions, classes, loops, arrays, file paths, and structured project folders.\u003c\/p\u003e\u003cp\u003eThe four main flight projects build on one another: hovering, waypoint navigation, obstacle avoidance, and autonomous landing. Each project is trained, tested, measured, and improved before becoming part of the next stage.\u003c\/p\u003e\u003cp\u003eSafety remains central throughout. You will not place an untested learning policy in direct control of a powered aircraft. Training begins in simulation, and later hardware transfer is treated as a staged process using conservative limits, propeller-off bench checks, flight boundaries, manual override, emergency stop, and local aviation rules.\u003c\/p\u003e\u003cb\u003eBuild the learning environment, train the agent, measure the result, and develop drone-control skills one safe experiment at a time.\u003c\/b\u003e","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":48214736175255,"sku":"9798189398582","price":1611.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798189398582.webp?v=1788730850","url":"https:\/\/atlanticbooks.com\/products\/reinforcement-learning-with-python-for-drones-train-autonomous-quadcopters-to-hover-navigate-and-land-using-self-learning-ai-algorithms-9798189398582","provider":"Atlantic Books","version":"1.0","type":"link"}