For episodic tasks with an absorbing state, why can't we both have $\gamma=1$ and $T= \infty$ in the definition of the return?
Why is it useful to define the return as the sum of the rewards from time $t$ onward rather than up to $t$?
When updating the state-action value in the Monte Carlo method, is the return the same for each state-action pair?
In the cross-entropy method, should I select state-action pairs by their immediate reward or by the episode reward?
When learning off-policy with multi-step returns, why do we use the current behaviour policy in importance sampling?
Shouldn't expected return be calculated for some faraway time in the future $t+n$ instead of current time $t$?
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