The Importance of Being Discrete: Learning Classes of Actions and Outcomes through Interaction

A robotic agent experiences a world of continuous multivariate sensations and chooses its actions from continuous action spaces. Unless the agent is able to successfully partition these into functionally similar classes, its ability to interact with the world will be extremely limited. We present a method whereby an unsupervised robotic agent learns to discriminate discrete actions out of its continuous action parameters. These actions are discriminated because they lead to qualitatively distinct outcomes in the robot's sensor space. Once found, these actions can be used by the robot as primitives for further exploration of its world. We present results gathered using a Pioneer 1 mobile robot.