Isbn: 9798293161522 - hands-on reinforcement learning for autonomous ai agents: practical python techniques for real-world solutions: 4 (the robust agent series) (9 resultados)

Condición: New.

Condición: As New. Unread book in perfect condition.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Condición: New.

Condición: As New. Unread book in perfect condition.

Paperback. Condición: new. Paperback. Hands-On Reinforcement Learning for Autonomous AI Agents: Practical Python Techniques for Real-World Solutions Are 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. This 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. What you'll gain: * Proficiency in Python-powered RL frameworks (Gymnasium, Stable Baselines3, PyTorch)* Ability to implement, tune, and evaluate agents for tasks ranging from discrete games to continuous control* Expertise in safe exploration, reward shaping, and preventing reward hacking in complex environments* Strategies for scalable deployment: Docker containers, Kubernetes orchestration, and edge inference with ONNX and quantized models* Skills in interpreting agent behavior using SHAP, saliency maps, and human-in-the-loop feedback pipelines Whether 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. Ready 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condición: New. Print on Demand.

Paperback. Condición: new. Paperback. Hands-On Reinforcement Learning for Autonomous AI Agents: Practical Python Techniques for Real-World Solutions Are 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. This 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. What you'll gain: * Proficiency in Python-powered RL frameworks (Gymnasium, Stable Baselines3, PyTorch)* Ability to implement, tune, and evaluate agents for tasks ranging from discrete games to continuous control* Expertise in safe exploration, reward shaping, and preventing reward hacking in complex environments* Strategies for scalable deployment: Docker containers, Kubernetes orchestration, and edge inference with ONNX and quantized models* Skills in interpreting agent behavior using SHAP, saliency maps, and human-in-the-loop feedback pipelines Whether 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. Ready 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…