2 citations · 2 across the 1 of their papers we have counts for
2 papers
eess.SY2019
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…
eess.SY2019★ 2 cited
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…