18 citations · 28 across the 14 of their papers we have counts for
3 papers · 1 filter
Coarse-Grained Smoothness for RL in Metric Spaces
Omer Gottesman, Kavosh Asadi, Cameron Allen +3
Principled decision-making in continuous state--action spaces is impossible without some assumptions. A common approach is to assume Lipschitz continuity of the Q-function. We show…
Bad-Policy Density: A Measure of Reinforcement Learning Hardness
David Abel, Cameron Allen, Dilip Arumugam +3
Reinforcement learning is hard in general. Yet, in many specific environments, learning is easy. What makes learning easy in one environment, but difficult in another? We address t…
Learning Markov State Abstractions for Deep Reinforcement Learning
Cameron Allen, Neev Parikh, Omer Gottesman +1
A fundamental assumption of reinforcement learning in Markov decision processes (MDPs) is that the relevant decision process is, in fact, Markov. However, when MDPs have rich obser…