21 citations · 141 across the 47 of their papers we have counts for
6 papers · 1 filter
The Geometry of Learning to Avoid Interventions
Ethan Pronovost, Khimya Khetarpal, Siddhartha Srinivasa
Human interventions are a common source of supervision in autonomous systems during deployment. Many existing approaches are based on avoiding interventions, yet the consequences o…
Faster Policy Learning with Continuous-Time Gradients
Samuel Ainsworth, Kendall Lowrey, John Thickstun +2
We study the estimation of policy gradients for continuous-time systems with known dynamics. By reframing policy learning in continuous-time, we show that it is possible construct…
Mo' States Mo' Problems: Emergency Stop Mechanisms from Observation
Samuel Ainsworth, Matt Barnes, Siddhartha Srinivasa
In many environments, only a relatively small subset of the complete state space is necessary in order to accomplish a given task. We develop a simple technique using emergency sto…
Imitation Learning as -Divergence Minimization
Liyiming Ke, Sanjiban Choudhury, Matt Barnes +3
We address the problem of imitation learning with multi-modal demonstrations. Instead of attempting to learn all modes, we argue that in many tasks it is sufficient to imitate any…
The Assistive Multi-Armed Bandit
Lawrence Chan, Dylan Hadfield-Menell, Siddhartha Srinivasa +1
Learning preferences implicit in the choices humans make is a well studied problem in both economics and computer science. However, most work makes the assumption that humans are a…
Bayes-CPACE: PAC Optimal Exploration in Continuous Space Bayes-Adaptive Markov Decision Processes
Gilwoo Lee, Sanjiban Choudhury, Brian Hou +1
We present the first PAC optimal algorithm for Bayes-Adaptive Markov Decision Processes (BAMDPs) in continuous state and action spaces, to the best of our knowledge. The BAMDP fram…