Abstract
This paper describes a new self-adapting control algorithm for
reactive autonomous agents. The architecture of the autonomous agents
integrates the reactive behavior with reinforcement learning. We show
how these components perform on-line adaptation of the autonomous
agents to various complex navigation situations by constructing an
internal model of the environment. Also, a discussion on cooperation
and coordination of teams of agents is presented.