58 citations · 85 across the 3 of their papers we have counts for
4 papers
Composing Meta-Policies for Autonomous Driving Using Hierarchical Deep Reinforcement Learning
Richard Liaw, Sanjay Krishnan, Animesh Garg +3
Rather than learning new control policies for each new task, it is possible, when tasks share some structure, to compose a "meta-policy" from previously learned policies. This pape…
BoostClean: Automated Error Detection and Repair for Machine Learning
Sanjay Krishnan, Michael J. Franklin, Ken Goldberg +1
Predictive models based on machine learning can be highly sensitive to data error. Training data are often combined with a variety of different sources, each susceptible to differe…
DDCO: Discovery of Deep Continuous Options for Robot Learning from Demonstrations
Sanjay Krishnan, Roy Fox, Ion Stoica +1
An option is a short-term skill consisting of a control policy for a specified region of the state space, and a termination condition recognizing leaving that region. In prior work…
HIRL: Hierarchical Inverse Reinforcement Learning for Long-Horizon Tasks with Delayed Rewards
Sanjay Krishnan, Animesh Garg, Richard Liaw +3
Reinforcement Learning (RL) struggles in problems with delayed rewards, and one approach is to segment the task into sub-tasks with incremental rewards. We propose a framework call…