41 citations · 81 across the 11 of their papers we have counts for
4 papers · 1 filter
Near-optimal Policy Identification in Active Reinforcement Learning
Xiang Li, Viraj Mehta, Johannes Kirschner +5
Many real-world reinforcement learning tasks require control of complex dynamical systems that involve both costly data acquisition processes and large state spaces. In cases where…
Exploration via Planning for Information about the Optimal Trajectory
Viraj Mehta, Ian Char, Joseph Abbate +5
Many potential applications of reinforcement learning (RL) are stymied by the large numbers of samples required to learn an effective policy. This is especially true when applying…
How Useful are Gradients for OOD Detection Really?
Conor Igoe, Youngseog Chung, Ian Char +1
One critical challenge in deploying highly performant machine learning models in real-life applications is out of distribution (OOD) detection. Given a predictive model which is ac…
BATS: Best Action Trajectory Stitching
Ian Char, Viraj Mehta, Adam Villaflor +2
The problem of offline reinforcement learning focuses on learning a good policy from a log of environment interactions. Past efforts for developing algorithms in this area have rev…