What is the purpose of “policy evaluation” in Markov Decision Processes?
a) To determine the number of states in the system
b) To calculate the reward for a single step
c) To estimate the value function for a given policy
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The correct answer is:
b) To assign values to each possible state-action pair
In Markov Decision Processes (MDPs), the reward matrix assigns a value (or reward) to each possible state-action pair. This helps in evaluating the immediate benefit or cost of taking a specific action in a given state, which is essential for determining the optimal policy and making decisions to maximize long-term rewards.