Which of the following statements is true about state value function

approximation using stochastic gradient descent?

w$\_($t$+1)=$w$\_$t$+\backslash$alpha $[$U$\_$t$-$v $($S$\_($t$+1),$w$)]$w$[$v $($s$,$w$)]$

Select all that apply.

a$.$ Semi$-$gradient TD$(0)$ methods typically learn faster than gradient Monte Carlo

methods

b$.$ When using U$\_$t$=$R$\_($t$+1)+$v $($S$\_($t$+1),$w$),$ the weight update is not using the true

gradient of the TD error.

c$.$ Using the Monte Carlo return or true value function as target results in an

unbiased update