Toward learning to control robot behaviors
It is important to methodologically distinguish between the environment dynamics, the agent dynamics and their coupling. In order to make this distinction clearer, let us define the following vocabulary. We will call a sensory-motor loop the internal mechanism linking perception to command whether fixed or adaptive and however sophisticated the percepts and commands can be for the sake of generality. We will call behavior the externally observed behavior tin the intuitive sense) produced by the execution of such a sensory-motor loop in a particular context. The originality of this work is, first, the formulation of reinforcement learning based on the sensory-motor dynamics and not the environment dynamics, and, second, to present a flexible selection learning mechanism of several sensory-motor loops and its formulation as a case of adaptive optimal control. Selection is presented as a way to coordinate independently developed sensory-motor loops.
reinforcement learning autonomous systems behavior coordination behavior-based robotics