Intrinsically Motivated Exploration of Learned Goal Spaces.

ABSTRACT: Finding algorithms that allow agents to discover a wide variety of skills efficiently and autonomously, remains a challenge of Artificial Intelligence. Intrinsically Motivated Goal Exploration Processes (IMGEPs) have been shown to enable real world robots to learn repertoires of policies producing a wide range of diverse effects. They work by enabling agents to autonomously sample goals that they then try to achieve. In practice, this strategy leads to an efficient exploration of complex environments with high-dimensional continuous actions. Until recently, it was necessary to provide the agents with an engineered goal space containing relevant features of the environment. In this article we show that the goal space can be learned using deep representation learning algorithms, effectively

SUBMITTER: Laversanne-Finot A 

PROVIDER: S-EPMC7835425 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature