Approximate Dynamic Programming For Linear Systems with State and Input Constraints
Systems and Control
2019-06-28 v1 Machine Learning
Systems and Control
Dynamical Systems
Optimization and Control
Abstract
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-critical applications. This paper leverages invariant sets to update control policies within an approximate dynamic programming (ADP) framework that guarantees constraint satisfaction for all time and converges to the optimal policy (in a linear quadratic regulator sense) asymptotically. An algorithm for implementing the proposed constrained ADP approach in a data-driven manner is provided. The potential of this formalism is demonstrated via numerical examples.
Cite
@article{arxiv.1906.11369,
title = {Approximate Dynamic Programming For Linear Systems with State and Input Constraints},
author = {Ankush Chakrabarty and Rien Quirynen and Claus Danielson and Weinan Gao},
journal= {arXiv preprint arXiv:1906.11369},
year = {2019}
}
Comments
Short version appeared in European Control Conference, 2019, Naples, Italy