NO.PZ2023091601000107
问题如下：
suppose that there are four states and three
actions, and that the current Q(S, A) values are as indicated in Table 14.2.
a. Suppose that on the next trial, Action 3 is
taken in State 4 and the total subsequent reward is 1.0. If α = 0.05, what will
the value of Q(4,3) be updated using Monte Carlo method ?
b. Suppose that the next decision
that has to be made on the trial we are considering turns out to be when we are
in State 3. Suppose further that a reward of 0.2 is earned between the two
decisions. Using the temporal difference method, we would note that the value
of being in State 3 is currently estimated to be 0.9. If a = 0.05, what will
the value of Q(4,3) be updated using Temporal difference learning?
解释：
a.If α = 0.05, the Monte Carlo method would lead to Q(4,3) being
updated from 0.8 to: 0.8 + 0.05(1.0 − 0.8) = 0.81
b. Suppose that when we take action A in state S we move to state S'.
We can use the current value for V(S’) to update as follows:
Qnew(S,A) = Qold(S,A) +α[R+γV(S') -
Qold(S,A)]
where R is the reward at the next step and γ
is the discount factor.
Thus, in this example, the temporal
difference method would lead to Q(4,3) being updated from 0.8 to: 0.8 +
0.05(0.2 + 0.9 − 0.8) = 0.815
公式是什么谢谢 什么考点
