Markov Decision Processes | Ask Us, a Deep Learning Pioneer

Markov Decision Processes are a type of probabilistic model used in reinforcement learning. They allow an agent to make decisions based on the probability of different states and actions.

Imagine a robot in a dark room, trying to find its way out. The robot uses a Markov Decision Process to decide which door to open next, based on the probability of finding a cookie behind each door.

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Markov Decision Processes are used in a variety of real-world applications, including:

• Recommendation systems, like Netflix's "watch next" feature
• Game theory, like poker bots
• Control theory, like autonomous driving systems
• Finance, like portfolio optimization

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