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Found 3 papers, 1 papers with code
Policy Optimization for Markovian Jump Linear Quadratic Control: Gradient-Based Methods and Global Convergence
In this paper, we investigate the global convergence of gradient-based policy optimization methods for quadratic optimal control of discrete-time Markovian jump linear systems (MJLS).
Policy Learning of MDPs with Mixed Continuous/Discrete Variables: A Case Study on Model-Free Control of Markovian Jump Systems
We implement the (data-driven) natural policy gradient method on different MJLS examples.
Convergence Guarantees of Policy Optimization Methods for Markovian Jump Linear Systems
Recently, policy optimization for control purposes has received renewed attention due to the increasing interest in reinforcement learning.
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