I am having trouble understanding the following problem and Q learning in general.
What I know so far about Q learning is that Q-learning is a model free method, i.e., it doesn’t need to learn P(s’|s,a). Thus it can compare the expected utility of the available actions without the model of the environment.It will also eventually find a policy that is the maximum achievable. However, I am not able to use this knowledge to do this problem. The answer is given in red.
If we are to look at time t = 1, then the equation becomes the following: $$ Q_{t+1}(s,a)=(1-1/2)(1)+1/2[R(s,a,s^{'})+(1) maxQ(s^{'},a^{'})] $$ How do I proceed from here?