3 accepted papers
Autonomous driving demands reinforcement learning (RL) agents that are not only performant, but also stable, sample-efficient, and robust to uncertainty. However, conventional policy optimization methods often suffer from unstable convergence, sensitivity to reward scaling, and limited generalizatio…
Partially-Observed Boolean dynamical systems (POBDS) are a general class of nonlinear state-space models consisting of a hidden Boolean state process observed through an arbitrary noisy mapping to a measurement space. The huge uncertainty present in systems/processes, along with the time-limit const…
We propose a Bayesian decision making framework for control of Markov Decision Processes (MDPs) with unknown dynamics and large, possibly continuous, state, action, and parameter spaces in data-poor environments. Most of the existing adaptive controllers for MDPs with unknown dynamics are based on t…