3 citations · 7 across the 10 of their papers we have counts for
5 papers · 1 filter
Decoupled Continuous-Time Reinforcement Learning via Hamiltonian Flow
Minh Nguyen
Many real-world control problems, ranging from finance to robotics, evolve in continuous time with non-uniform, event-driven decisions. Standard discrete-time reinforcement learnin…
GRL-SNAM: Geometric Reinforcement Learning with Path Differential Hamiltonians for Simultaneous Navigation and Mapping in Unknown Environments
Aditya Sai Ellendula, Yi Wang, Minh Nguyen +1
We present GRL-SNAM, a geometric reinforcement learning framework for Simultaneous Navigation and Mapping(SNAM) in unknown environments. A SNAM problem is challenging as it needs t…
Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data
Luke McLennan, Yi Wang, Ryan Farell +2
We introduce a robust framework for learning various generalized Hamiltonian dynamics from noisy, sparse phase-space data and in an unsupervised manner based on variational Bayesia…
A Differential and Pointwise Control Approach to Reinforcement Learning
Minh Nguyen, Chandrajit Bajaj
Reinforcement learning (RL) in continuous state-action spaces remains challenging in scientific computing due to poor sample efficiency and lack of pathwise physical consistency. W…
Motion Code: Robust Time Series Classification and Forecasting via Sparse Variational Multi-Stochastic Processes Learning
Chandrajit Bajaj, Minh Nguyen
Despite extensive research, time series classification and forecasting on noisy data remain highly challenging. The main difficulties lie in finding suitable mathematical concepts…