Autonomous Dispatch of Fleet Vehicles

Non-transitory computer readable media for use with a processor, the computer readable media having stored thereon instructions that direct the processor to:

• obtain data corresponding to actions and at least one state of one or more agent in an environment and to obtain data corresponding to states of at least one variable within the environment;
• execute a Q-learning algorithm, wherein the algorithm includes:
• computing and assigning initial Q-values for the one or more agent, wherein:
• an initial Q-value corresponding to each of one or more obtainable action-states and
• an initial Q-value for the at least one state of the one or more agent are assigned non-negative values;
• and
• an initial Q-value for an unobtainable action-state is assigned a large negative value;
• implementing an exploration period comprising a selected number of actions of the one or more agent, wherein after each action is completed a reward is calculated based on the at least one state of the one or more agent and the state of the at least one variable in the environment, and the reward is used to update the Q-value corresponding to the action-state;
• obtaining an optimized set of Q-values for the one agent after the exploration period;
• applying the optimized set of Q-values to the plurality of agents in the fleet, and using sensors to obtain data corresponding to actions and at least one state of the plurality of agents in the environment and to obtain data corresponding to states of at least one variable within the environment;
• and
• further optimizing the Q-values for the plurality of agents, wherein for each agent, after an action is completed a reward is calculated based on the at least one state of the agent and the state of the at least one variable in the environment, and the reward is used to update the Q-value corresponding to the action-state.