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What is the expectation of an empirical model in model based RL?

Artificial Intelligence Asked by ijuneja on November 4, 2021

In the paper – "Action Elimination and Stopping Conditions for the Multi-Armed Bandit and Reinforcement Learning Problems", on page 1083, on the 6th line from the bottom, the authors define expectation of the empirical model as
$$hat{mathbb{E}}_{s,s’,a}[V(s’)] = sum_{s’ in S} hat{P}^{a}_{s, s’}V(s’).$$
I didn’t understand the significance of this quantity since it puts $V(s’)$ inside an expectation while assuming the knowledge of $V(s’)$ in the definition on the right.

A clarification in this regard would be appreciated.

EDIT:
The paper defines $hat{P}^{a}_{s, s’}$ as,
$$hat{P}^{a}_{s, s’} = frac{|(s, a, s’, t)|}{|(s, a, t)|}.$$
Where $|(s, a, t)|$ is the number of times state $s$ was visited and action $a$ was taken and $|(s, a, s’, t)|$ as the number of times among the $|(s, a, t)|$ times $(s, a)$ was visited when the next state landed in was $s’$ during model learning.

No explicit definition for $V$ is provided however, $V^{pi}$ is defined as the usual expected discounted return, using the same definition as Sutton and Barto or other sources.

One Answer

If I understand your question correctly, the significance of this is due to the fact that $s'$ is random. In the RHS of the equation it is assumed that $V(cdot)$ is known for each state, but the quantity is measuring the expected value of the next state given the current state and action.

Answered by harwiltz on November 4, 2021

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