32 citations · 61 across the 9 of their papers we have counts for
4 papers · 1 filter
What can I do here? A Theory of Affordances in Reinforcement Learning
Khimya Khetarpal, Zafarali Ahmed, Gheorghe Comanici +2
Reinforcement learning algorithms usually assume that all actions are always available to an agent. However, both people and animals understand the general link between the feature…
The Efficiency of Human Cognition Reflects Planned Information Processing
Mark K. Ho, David Abel, Jonathan D. Cohen +2
Planning is useful. It lets people take actions that have desirable long-term consequences. But, planning is hard. It requires thinking about consequences, which consumes limited c…
Learning State Abstractions for Transfer in Continuous Control
Kavosh Asadi, David Abel, Michael L. Littman
Can simple algorithms with a good representation solve challenging reinforcement learning problems? In this work, we answer this question in the affirmative, where we take "simple…
Lipschitz Lifelong Reinforcement Learning
Erwan Lecarpentier, David Abel, Kavosh Asadi +3
We consider the problem of knowledge transfer when an agent is facing a series of Reinforcement Learning (RL) tasks. We introduce a novel metric between Markov Decision Processes (…