ISBN: 9798291998656 - reinforcement learning simplified: an introduction to teaching ai through rewards and trials (9 resultados)

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Paperback. Condición: new. Paperback. How do you teach an AI to make smart decisions on its own? You reward it.Reinforcement Learning Simplified is a beginner-friendly introduction to one of the most fascinating fields in artificial intelligence-where machines learn not from data alone, but from experience, feedback, and trial and error. From training agents to play games, navigate environments, or optimize real-world systems, this book explains core concepts in plain language with practical Python examples.No heavy math or academic jargon. Just the foundations you need to understand how reinforcement learning works-and how to build and experiment with your own agents.Inside, you'll learn how to: Understand key ideas like agents, environments, rewards, and policiesBuild simple RL simulations from scratch in PythonExplore core algorithms like Q-learning, SARSA, and Deep Q-Networks (DQN)Visualize how agents learn over timeApply RL to small games, grid environments, and decision-making tasksUse libraries like gym, stable-baselines3, and PyTorch for hands-on developmentUnderstand the role of exploration vs. exploitationTune hyperparameters and avoid common training pitfallsWhether you're a student, hobbyist, or aspiring AI developer, Reinforcement Learning Simplified is the perfect first step into a field that's powering the next generation of intelligent systems-from robotics to self-driving cars to recommendation engines. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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Paperback. Condición: new. Paperback. How do you teach an AI to make smart decisions on its own? You reward it.Reinforcement Learning Simplified is a beginner-friendly introduction to one of the most fascinating fields in artificial intelligence-where machines learn not from data alone, but from experience, feedback, and trial and error. From training agents to play games, navigate environments, or optimize real-world systems, this book explains core concepts in plain language with practical Python examples.No heavy math or academic jargon. Just the foundations you need to understand how reinforcement learning works-and how to build and experiment with your own agents.Inside, you'll learn how to: Understand key ideas like agents, environments, rewards, and policiesBuild simple RL simulations from scratch in PythonExplore core algorithms like Q-learning, SARSA, and Deep Q-Networks (DQN)Visualize how agents learn over timeApply RL to small games, grid environments, and decision-making tasksUse libraries like gym, stable-baselines3, and PyTorch for hands-on developmentUnderstand the role of exploration vs. exploitationTune hyperparameters and avoid common training pitfallsWhether you're a student, hobbyist, or aspiring AI developer, Reinforcement Learning Simplified is the perfect first step into a field that's powering the next generation of intelligent systems-from robotics to self-driving cars to recommendation engines. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…