Incremental Learning of Skill Collections based on Intrinsic Motivation
In Frontiers in Neurorobotics, o.A., volume 7, number 11, pages 1-12, 2013.
Life-long learning of reusable, versatile skills is a key prerequisite for embodied agents that act in a complex, dynamic environment and are faced with different tasks over their lifetime. We address the question of how an agent can learn useful skills efficiently during
a developmental period, i.e., when no task is imposed on him and no external reward signal is provided. Learning of skills in a developmental period needs to be incremental and self-motivated. We propose a new incremental, task-independent skill discovery
approach that is suited for continuous domains. Furthermore, the agent learns specific skills based on intrinsic motivation mechanisms that determine on which skills learning is focused at a given point in time. We evaluate the approach in a reinforcement learning setup
in two continuous domains with complex dynamics. We show that an intrinsically motivated, skill learning agent outperforms an agent which learns task solutions from scratch. Furthermore, we compare different intrinsic motivation mechanisms and how efficiently they
make use of the agent´s developmental period.
Hierarchical Reinforcement Learning, Skill Discovery, Intrinsic Motivation, Life-long Learning, Graph-based Representation