On Ensuring that Intelligent Machines Are Well-Behaved

no code implementations • 17 Aug 2017 • Philip S. Thomas, Bruno Castro da Silva, Andrew G. Barto, Emma Brunskill

We propose a new framework for designing machine learning algorithms that simplifies the problem of specifying and regulating undesirable behaviors.

BIG-bench Machine Learning

Clustering via Dirichlet Process Mixture Models for Portable Skill Discovery

no code implementations • NeurIPS 2011 • Scott Niekum, Andrew G. Barto

Skill discovery algorithms in reinforcement learning typically identify single states or regions in state space that correspond to task-specific subgoals.

Clustering Reinforcement Learning (RL)

Constructing Skill Trees for Reinforcement Learning Agents from Demonstration Trajectories

no code implementations • NeurIPS 2010 • George Konidaris, Scott Kuindersma, Roderic Grupen, Andrew G. Barto

We demonstrate that CST constructs an appropriate skill tree that can be further refined through learning in a challenging continuous domain, and that it can be used to segment demonstration trajectories on a mobile manipulator into chains of skills where each skill is assigned an appropriate abstraction.

reinforcement-learning Reinforcement Learning (RL)

Skill Discovery in Continuous Reinforcement Learning Domains using Skill Chaining

no code implementations • NeurIPS 2009 • George Konidaris, Andrew G. Barto

We introduce skill chaining, a skill discovery method for reinforcement learning agents in continuous domains, that builds chains of skills leading to an end-of-task reward.

reinforcement-learning Reinforcement Learning (RL)

Skill Characterization Based on Betweenness

no code implementations • NeurIPS 2008 • Özgür Şimşek, Andrew G. Barto

We present a characterization of a useful class of skills based on a graphical representation of an agent's interaction with its environment.