In this article we introduce the Arcade Learning Environment (ALE): both a challenge
problem and a platform and methodology for evaluating the development of general, domain …

Learning from Demonstration (LfD) explores techniques for learning a task policy from
examples provided by a human teacher. The field of LfD has grown into an extensive body …

As computational agents are increasingly used beyond research labs, their success will
depend on their ability to learn new skills and adapt to their dynamic, complex environments …

This paper addresses the challenge of learning to play many different video games with little
domain-specific knowledge. Specifically, it introduces a neuroevolution approach to general …

Contingency awareness is the recognition that some aspects of a future observation are
under an agent's control while others are solely determined by the environment. This paper …

State abstraction can give rise to models of environments that are both compressed and
useful, thereby enabling efficient sequential decision making. In this work, we offer the first …

The societal relevance of artificial intelligence is growing rapidly. Advances are primarily
driven by machine learning techniques. Recently, many educational tools for teaching AI …

We propose a novel hierarchical reinforcement learning framework for control with
continuous state and action spaces. In our framework, the user specifies subgoal regions …

The ability to generalize is an important feature of any intelligent agent. Not only because it
may allow the agent to cope with large amounts of data, but also because in some …

Robots and other computational agents are increasingly becoming part of our daily lives.
They will need to be able to learn to perform new tasks, adapt to novel situations, and …