no code implementations • 28 Sep 2023 • Xubo Lyu, Hanyang Hu, Seth Siriya, Ye Pu, Mo Chen
We present task-oriented Koopman-based control that utilizes end-to-end reinforcement learning and contrastive encoder to simultaneously learn the Koopman latent embedding, operator, and associated linear controller within an iterative loop.
no code implementations • 29 Mar 2022 • Xubo Lyu, Amin Banitalebi-Dehkordi, Mo Chen, Yong Zhang
In complex problems with large state and action spaces, it is advantageous to extend MAPG methods to use higher-level actions, also known as options, to improve the policy search efficiency.
Hierarchical Reinforcement Learning Multi-agent Reinforcement Learning +3
no code implementations • 4 Nov 2020 • Xubo Lyu, Site Li, Seth Siriya, Ye Pu, Mo Chen
On the other end, "classical methods" such as optimal control generate solutions without collecting data, but assume that an accurate model of the system and environment is known and are mostly limited to problems with low-dimensional (lo-dim) state spaces.