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Reinforcement Learning in Practice
Reinforcement Learning in Practice
Price
$ 2,000.00
Description
This advanced course introduces reinforcement learning (RL), where agents learn optimal behaviors through trial and error. It begins with core concepts like Markov Decision Processes (MDPs), policies, value functions, and Q-learning. Learners implement RL algorithms using Python and libraries such as OpenAI Gym and TensorFlow. Projects include teaching agents to play games, navigate environments, and make dynamic decisions in changing scenarios. The course also explores deep reinforcement learning techniques using Deep Q-Networks (DQNs). It emphasizes tuning rewards, exploration strategies, and training stability. This course is perfect for advanced machine learning practitioners, AI researchers, and robotics developers.
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斎藤 彩
とても分かりやすくて勉強になりました！
Jack Moore
初心者にもやさしい説明で、助かりました。
高橋 拓也
講師の説明が丁寧で理解しやすかったです。
Emily Harris
This course was perfect for a beginner like me. Clear and engaging!