313 citations · 498 across the 41 of their papers we have counts for
4 papers · 1 filter
Marginalized Importance Sampling for Off-Environment Policy Evaluation
Pulkit Katdare, Nan Jiang, Katherine Driggs-Campbell
Reinforcement Learning (RL) methods are typically sample-inefficient, making it challenging to train and deploy RL-policies in real world robots. Even a robust policy trained in si…
Monte-Carlo Tree Search for Policy Optimization
Xiaobai Ma, Katherine Driggs-Campbell, Zongzhang Zhang +1
Gradient-based methods are often used for policy optimization in deep reinforcement learning, despite being vulnerable to local optima and saddle points. Although gradient-free met…
Improved Robustness and Safety for Autonomous Vehicle Control with Adversarial Reinforcement Learning
Xiaobai Ma, Katherine Driggs-Campbell, Mykel J. Kochenderfer
To improve efficiency and reduce failures in autonomous vehicles, research has focused on developing robust and safe learning methods that take into account disturbances in the env…
EnsembleDAgger: A Bayesian Approach to Safe Imitation Learning
Kunal Menda, Katherine Driggs-Campbell, Mykel J. Kochenderfer
While imitation learning is often used in robotics, the approach frequently suffers from data mismatch and compounding errors. DAgger is an iterative algorithm that addresses these…