4 papers
Lagrangian-based Equilibrium Propagation: generalisation to arbitrary boundary conditions & equivalence with Hamiltonian Echo Learning
Guillaume Pourcel, Debabrota Basu, Maxence Ernoult +1
Equilibrium Propagation (EP) is a learning algorithm for training Energy-based Models (EBMs) on static inputs which leverages the variational description of their fixed points. Ext…
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…
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…
How does Your RL Agent Explore? An Optimal Transport Analysis of Occupancy Measure Trajectories
Reabetswe M. Nkhumise, Debabrota Basu, Tony J. Prescott +1
The rising successes of RL are propelled by combining smart algorithmic strategies and deep architectures to optimize the distribution of returns and visitations over the state-act…