why use state value in reinforcement learning why can't we use a state model in reinforcement learning why would we need a state model in reinforcement learning? why can't one use an implicit state-action function? when the state function is unknown, the model must be used to why not compute q(s,a) state-action function which statement is true of an example of an example of a state model why does reinforcement learning require a state what is q(s,a) state action what is the purpose of q(s,a) why can't one just use bellman's equation to decide what the best policy why can't one just use a form of bellman's equation in V(s) to determine the best policy via e.g. why do we need to compute states why do we compute the state-action function without a state-model why use state and action in reinforcement learning why do we use a state-action function to specify a policy in reinforcement learning why do you use q(s,a) in reinforcement learning why do we need a state model why cannot we use the state in training without a model why is q(s,a) state-action function necessary for an implementation