{"product_id":"hands-on-reinforcement-learning-for-autonomous-ai-agents-practical-python-techniques-for-real-world-solutions-9798293161522","title":"Hands-On Reinforcement Learning for Autonomous AI Agents: Practical Python Techniques for Real-World Solutions","description":"\u003cp\u003e • Author(s): Ethan Tyson\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Robotics\u003c\/p\u003e\u003cp\u003e\u003cb\u003eHands-On Reinforcement Learning for Autonomous AI Agents: Practical Python Techniques for Real-World Solutions\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003eAre you ready to transform your ideas into intelligent, self-learning systems that solve real-world problems? **Hands-On Reinforcement Learning for Autonomous AI Agents** delivers the practical Python techniques you need to build, train, and deploy agents that adapt and excel in dynamic environments. \u003cp\u003e\u003c\/p\u003eThis book shows you how to master reinforcement learning from the ground up. You'll explore foundational methods-like tabular Q-Learning and Deep Q-Networks-before advancing to policy-based algorithms such as PPO, A2C, and SAC. You'll discover how to leverage cutting-edge architectures like Dreamer's world models and Decision Transformers, and orchestrate multi-agent ecosystems with PettingZoo and Ray RLlib. Every chapter is packed with real-world code examples, detailed explanations, and hands-on projects-from traffic signal control to warehouse robotics and beyond. \u003cp\u003e\u003c\/p\u003e\u003cb\u003eWhat you'll gain: \u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e* Proficiency in Python-powered RL frameworks (Gymnasium, Stable Baselines3, PyTorch)\u003cbr\u003e* Ability to implement, tune, and evaluate agents for tasks ranging from discrete games to continuous control\u003cbr\u003e* Expertise in safe exploration, reward shaping, and preventing reward hacking in complex environments\u003cbr\u003e* Strategies for scalable deployment: Docker containers, Kubernetes orchestration, and edge inference with ONNX and quantized models\u003cbr\u003e* Skills in interpreting agent behavior using SHAP, saliency maps, and human-in-the-loop feedback pipelines \u003cp\u003e\u003c\/p\u003eWhether you're an AI engineer, robotics developer, or data scientist, this book empowers you to build robust, interpretable, and production-ready autonomous agents. Turn theoretical concepts into working solutions that drive efficiency and innovation across industries. \u003cp\u003e\u003c\/p\u003eReady to take control of your next reinforcement learning project? Add **Hands-On Reinforcement Learning for Autonomous AI Agents** to your toolkit today and start creating intelligent systems that learn, adapt, and deliver real-world impact.","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":47594792386711,"sku":"9798293161522","price":1522.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798293161522.webp?v=1774987174","url":"https:\/\/atlanticbooks.com\/products\/hands-on-reinforcement-learning-for-autonomous-ai-agents-practical-python-techniques-for-real-world-solutions-9798293161522","provider":"Atlantic Books","version":"1.0","type":"link"}