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What is Markov Decision Processes Explained about?

The document describes a Markov decision process (MDP) for an agent navigating a 4x3 grid environment. The agent must choose actions (up, right, down, left) each turn to reach a goal state with the highest reward. Due to unreliable actions, the optimal policy is not a fixed sequence but a policy that specifies the best action for any state. Solving the MDP involves finding the policy that yields the highest expected utility over possible environment histories.

Author

Abhishek Nanda

Language

EN