13 citations · 33 across the 15 of their papers we have counts for
3 papers · 1 filter
Safe Approximate Dynamic Programming Via Kernelized Lipschitz Estimation
Ankush Chakrabarty, Devesh K. Jha, Gregery T. Buzzard +2
We develop a method for obtaining safe initial policies for reinforcement learning via approximate dynamic programming (ADP) techniques for uncertain systems evolving with discrete…
Approximate Dynamic Programming For Linear Systems with State and Input Constraints
Ankush Chakrabarty, Rien Quirynen, Claus Danielson +1
Enforcing state and input constraints during reinforcement learning (RL) in continuous state spaces is an open but crucial problem which remains a roadblock to using RL in safety-c…
L2 Observers for a Class of Nonlinear Systems with Unknown Inputs
Martin Corless, Ankush Chakrabarty
We consider the problem of estimating the state and unknown input for a large class of nonlinear systems subject to unknown exogenous inputs. The exogenous inputs themselves are mo…