Brush up :)
The policy is optimized to maximize the the total reward, defined as the sum of the reward at each step, discounted by some factor.
The policy is optimized to maximize the the total reward, defined as the sum of the reward at each step, discounted by some factor.
To maximize its expected return it has to predict that taking action α in state s in time step t will produce state s' and reward r in time step t+1. Which btw it can predict by estimating a value function that sums over the expected return from time step t onwards. The value function essentially tells the policy what state it wants to be in and what action to take to get to that state, in order to eventually achieve its goal (represented by max cumulative reward at the end of an episode).
So, yes, like you say, the whole policy is a game result predictor but the decision making process is a next-move predictor. The policy must make the locally optimal decisions to get to the globally optimal result, if I may be so bold. It's the same problem with a different solution as in heuristic search, except you have a reward function instead of a heuristic cost function.
Sutton & Barto 2ed is free here btw:
https://web.stanford.edu/class/psych209/Readings/SuttonBarto...
The value function stuff is in Section 3.7. Now if someone could explain the grid world example in Figure 3.5 to me that would be great because I'm very confused about the fact that any action at state A or B takes the agent to state A' or B' respectively, since those are not even adjacent to A and B. I'll go ask chat I suppose.
And, the expected return from a state is often estimated rather than an explicit trajectory run out all the way. A value function can estimate expected return without explicitly predicting the future states or individual rewards that make up that return.
Fwiw, I use the phrase "total reward" above and use it as a synonym for "return", which is lazy use of language too.
>> And, the expected return from a state is often estimated rather than an explicit trajectory run out all the way. A value function can estimate expected return without explicitly predicting the future states or individual rewards that make up that return.
I think rather that's the job of the value function, to inform the policy of the, well, value of a state, or state-action pair. Somehow the policy needs to know what a good action is to take in the current state, or what is the best action to take. And of course it doesn't have to run the entire episode before it does that: but that's what I mean that it predicts the next move and not just the outcome of the entire game.