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let me summarize Where We Are if you can compute the state action value function Q of s a then it gives you a way to pick a good action from everything just pick the action a that gives you the largest value of Q of s a so the question is how do you compute these values Q of s a in reinforcement learning theres a key equation called the Bellman equation that will help us compute the state action value function lets take a look at what is this equation as a reminder this is the definition of q of sa its returned if you start the state s take the action a once and then behave optimally after that in order to describe the Bellman equation Im going to use the following notation Im going to use S to denote the current state next Im going to use R of s to denote the reward of the current state so for our low mdp example we would have that R of 1 State 1 is 100 the reward of State 2 is 0 and so on and the reward of State 6 is 40. Im going to use the alphabet a to denote the current ac
