2 papers
cs.LG2026
Neural ODE and SDE Models for Adaptation and Planning in Model-Based Reinforcement Learning
Chao Han, Stefanos Ioannou, Luca Manneschi +4
We investigate neural ordinary and stochastic differential equations (neural ODEs and SDEs) to model stochastic dynamics in fully and partially observed environments within a model…
cs.LG2024
Dynamical-VAE-based Hindsight to Learn the Causal Dynamics of Factored-POMDPs
Chao Han, Debabrota Basu, Michael Mangan +2
Learning representations of underlying environmental dynamics from partial observations is a critical challenge in machine learning. In the context of Partially Observable Markov D…