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

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.

• I think I can interpret this if the LHS was $\mathbb{\hat{E}}_{s,a}[V(s')]$ . . . does the paper definitely show $\mathbb{\hat{E}}_{s,s', a}$? Does the paper define $V$ in this context? – Neil Slater Jul 21 '20 at 13:20
• @NeilSlater the paper does use $s, s', a$ in the notation. have edited to add details – ijuneja Jul 21 '20 at 13:39

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.
• I think i understand. This would mean that $s'$ means different things on the left and the right in a sense. – ijuneja Jul 21 '20 at 17:07