6 citations · 11 across the 4 of their papers we have counts for
3 papers · 1 filter
Policy Optimization for Markovian Jump Linear Quadratic Control: Gradient-Based Methods and Global Convergence
Joao Paulo Jansch-Porto, Bin Hu, Geir Dullerud
Recently, policy optimization for control purposes has received renewed attention due to the increasing interest in reinforcement learning. In this paper, we investigate the global…
Policy Learning of MDPs with Mixed Continuous/Discrete Variables: A Case Study on Model-Free Control of Markovian Jump Systems
Joao Paulo Jansch-Porto, Bin Hu, Geir Dullerud
Markovian jump linear systems (MJLS) are an important class of dynamical systems that arise in many control applications. In this paper, we introduce the problem of controlling unk…
Convergence Guarantees of Policy Optimization Methods for Markovian Jump Linear Systems
Joao Paulo Jansch-Porto, Bin Hu, Geir Dullerud
Recently, policy optimization for control purposes has received renewed attention due to the increasing interest in reinforcement learning. In this paper, we investigate the conver…