I started today by describing a famous problem about searching for a randomly moving hidden object. This is an example of a partially observed Markov decision process. This problem has not yet been solved in full generality. However, the problem has been solved in continuous time. See R. R. Weber. Optimal search for a randomly moving object. J. Appl. Prob. 23:708-717, 1986. The reason the problem is easier in continuous time is that the state $x_1$ (the probability that the object is in location 1) changes continuously, rather than in jumps.
Let me emphasize that in Lecture 4, we used $F_s(x)$ to mean the value function of a MDP over $s$ steps, with $F_0(x)=0$. In the N case of $c(x,u)\geq 0$, $F_s(x)$ is clearly nondecreasing in $s$. By contrast, in this lecture, $F_s(x)$ was used to mean the minimum cost in a stopping problem in which we must stop within $s$ steps, and $F_0(x)=k(x)$. $F_s(x)$ is now clearly nonincreasing in $s$. That is because as $s$ increases we are being given more flexibility as to when we must stop. In both cases $F_s(x)$ satisfies an optimality equation and $F_s(x)\to$ a limit, say $F_\infty(x)$, but from a different direction.
Having completed these lectures you know all you need to do all questions on Examples Sheet 1.
Questions 7 and 8 use the idea of a one-step look-ahead (OSLA) rule that we have met in today's lecture. Question 8 is subtle, because although it is easy to guess the answer by thinking about a OSLA rule, how do you prove this answer is correct? Hint: use Theorem 4.2. Be careful when writing down the dynamic programming equation for Question 8 that you put the expected value (or integral and density function for an exponential random variable) in the right place relative to where you place your max{ , }).
