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12min chapter
773: Deep Reinforcement Learning for Maximizing Profits, with Prof. Barrett Thomas
Super Data Science: ML & AI Podcast with Jon Krohn
Understanding Markov Decision Processes in Reinforcement Learning
The chapter explores the fundamentals of Markov Decision Processes (MDPs) in reinforcement learning, focusing on states, actions, rewards, and future values through conditional expectations. Examples like stock market predictions and video game scenarios are used to illustrate MDP concepts. Challenges in specifying probability distributions, predicting demand, and achieving convergence with neural networks in reinforcement learning are also discussed.
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