1. A method of optimizing vehicle dispatching in a dynamic work area resulting from a linear program comprising: inputting a vehicle dispatching schedule for a plurality of vehicles as a state representation into a reinforcement learning algorithm, the state representation having a plurality of states, each state having a plurality of possible actions; using a computer, the computer comprising computer readable media programmed with a set of instructions causing the computer to perform the steps of: running a simulation of the states by selecting one of the possible actions within each state, one running of the simulation being an episode and producing a result based on a proximity to optimum performance of the plurality of vehicles; assigning a reward value based on the result; propagating the reward value back through the simulation with reference to time between states; for each action in each state within the episode, determining a policy value based on at least one of the reward value, a subsequent state, a subsequent action, elapsed time in the episode at the state and elapsed time in the episode at the subsequent state; developing a policy for each state in which the action in each state that produces a maximum policy value is a preferred action; dispatching the preferred action to the plurality of vehicles in the dynamic work area; and causing the plurality of vehicles to perform in accordance with the preferred action.