188 citations · 291 across the 32 of their papers we have counts for
5 papers · 1 filter
Learning Reward Machines: A Study in Partially Observable Reinforcement Learning
Rodrigo Toro Icarte, Ethan Waldie, Toryn Q. Klassen +3
Reinforcement learning (RL) is a central problem in artificial intelligence. This problem consists of defining artificial agents that can learn optimal behaviour by interacting wit…
Efficient Multi-agent Epistemic Planning: Teaching Planners About Nested Belief
Christian Muise, Vaishak Belle, Paolo Felli +4
Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents. However, pla…
AppBuddy: Learning to Accomplish Tasks in Mobile Apps via Reinforcement Learning
Maayan Shvo, Zhiming Hu, Rodrigo Toro Icarte +3
Human beings, even small children, quickly become adept at figuring out how to use applications on their mobile devices. Learning to use a new app is often achieved via trial-and-e…
Be Considerate: Objectives, Side Effects, and Deciding How to Act
Parand Alizadeh Alamdari, Toryn Q. Klassen, Rodrigo Toro Icarte +1
Recent work in AI safety has highlighted that in sequential decision making, objectives are often underspecified or incomplete. This gives discretion to the acting agent to realize…
LTL2Action: Generalizing LTL Instructions for Multi-Task RL
Pashootan Vaezipoor, Andrew Li, Rodrigo Toro Icarte +1
We address the problem of teaching a deep reinforcement learning (RL) agent to follow instructions in multi-task environments. Instructions are expressed in a well-known formal lan…