4 citations · 5 across the 3 of their papers we have counts for
3 papers
The Role of Lookahead and Approximate Policy Evaluation in Reinforcement Learning with Linear Value Function Approximation
Anna Winnicki, Joseph Lubars, Michael Livesay +1
Function approximation is widely used in reinforcement learning to handle the computational difficulties associated with very large state spaces. However, function approximation in…
Optimistic Policy Iteration for MDPs with Acyclic Transient State Structure
Joseph Lubars, Anna Winnicki, Michael Livesay +1
We consider Markov Decision Processes (MDPs) in which every stationary policy induces the same graph structure for the underlying Markov chain and further, the graph has the follow…
Combining Reinforcement Learning with Model Predictive Control for On-Ramp Merging
Joseph Lubars, Harsh Gupta, Sandeep Chinchali +4
We consider the problem of designing an algorithm to allow a car to autonomously merge on to a highway from an on-ramp. Two broad classes of techniques have been proposed to solve…