توضیحات
Mastering Reinforcement Learning with Python provides a comprehensive guide to developing intelligent agents capable of learning through trial and error. It covers core concepts such as Markov Decision Processes, Q-Learning, Deep Q-Networks, Policy Gradients, and advanced techniques like Actor-Critic methods. With Python-based examples and projects, the book enables readers to apply reinforcement learning to games, robotics, finance, and other real-world problems, combining theory with hands-on coding experience.
نقد و بررسیها
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