Abstract: An unmanned vehicle cooperation system for a person to grasp the operation of an unmanned vehicle and to perform a smooth cooperation action with the unmanned vehicle. The unmanned vehicle cooperation system performs a cooperation action involving human intervention between a plurality of agents including an unmanned vehicle. The unmanned vehicle has a processing unit that performs an action processing of the unmanned vehicle with artificial intelligence using a decision making model established by including a plurality of tasks, the plurality of tasks include a cooperation task for performing the cooperation action, and the processing unit executes a consensus building processing that builds a consensus as to execution of the cooperation task between the agents using a negotiation protocol, and a cooperation action processing that executes the cooperation action of the unmanned vehicle on the basis of the consented cooperation task.

Abstract: A learning device is configured to perform reinforcement learning of a policy of an agent by self-play under a multi-agent environment. The multi-agent environment is an asymmetric environment in which at least one of a type of an action performed by the agent, a type of a state acquired by the agent, and a definition of a reward given to the agent is different, and an evaluation index for learning is given to each of agents. The learning device performs performing learning of a predetermined agent of the agents by using a learning model, acquiring the evaluation index for the learning model of the predetermined agent after learning, comparing the evaluation index for the predetermined agent with the evaluation index for the other agent, and a step of setting the agent having a lower evaluation index as a learning target.