Reinforcement Learning with Python for Drones: Train Autonomous Quadcopters to Hover, Navigate, and Land Using Self-Learning AI Algorithms

Name: Reinforcement Learning with Python for Drones: Train Autonomous Quadcopters to Hover, Navigate, and Land Using Self-Learning AI Algorithms
Brand: Independently Published
SKU: 9798189398582
Price: 1611.0 INR
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by Nathan Westwood

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ISBN13: 9798189398582
Binding: Paperback
Subject: N/A
Publisher: Independently Published
Publisher Imprint: Independently Published
Publication Date: July 27, 2026
Pages: 206
Original Price: GBP 14.22
Language: English
Edition: N/A
Item Weight: 254 grams
BISAC Subject(s): Languages / Python

About The Book

Teach a simulated quadcopter to hover, navigate, avoid obstacles, and land through repeated practice.

A 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.

Reinforcement Learning with Python for Drones shows you how to train autonomous drone behaviours in simulation before moving cautiously toward real hardware.

Using Python, NumPy, Gymnasium, PyBullet, Matplotlib, and Stable-Baselines3, this beginner-friendly guide will help you:

Understand how quadcopters move, hover, turn, and respond to thrust
Learn the reinforcement-learning loop of observations, actions, rewards, steps, and episodes
Create a clean Python development environment for repeatable experiments
Build a reusable simulated drone environment with Gymnasium and PyBullet
Design useful observation spaces, action spaces, reward functions, and termination rules
Train a simple altitude-control agent with Q-learning
Move from Q-tables to neural-network-based learning
Train a hovering agent with Proximal Policy Optimisation, or PPO
Improve hovering under varied starting positions, sensor noise, wind, and mass changes
Train a drone to navigate toward waypoints
Teach an agent to avoid static and random obstacles
Build an autonomous landing task with alignment, descent, touchdown, and failure handling
Use domain randomisation to reduce the gap between simulation and reality
Evaluate results using hovering error, navigation success, collision rates, and landing accuracy
Compare DQN, PPO, and SAC for drone-control tasks
Save models, organise logs, plot results, and make experiments reproducible
Move cautiously through software-in-the-loop, bench testing, manual override, emergency stop, and controlled flight preparation

You 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.

The 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.

Safety 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.

Build the learning environment, train the agent, measure the result, and develop drone-control skills one safe experiment at a time.

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