3 citations · 11 across the 6 of their papers we have counts for
6 papers
A general class of surrogate functions for stable and efficient reinforcement learning
Sharan Vaswani, Olivier Bachem, Simone Totaro +6
Common policy gradient methods rely on the maximization of a sequence of surrogate functions. In recent years, many such surrogate functions have been proposed, most without strong…
Hierarchical Representation Learning for Markov Decision Processes
Lorenzo Steccanella, Simone Totaro, Anders Jonsson
In this paper we present a novel method for learning hierarchical representations of Markov decision processes. Our method works by partitioning the state space into subsets, and d…
Hierarchical reinforcement learning for efficient exploration and transfer
Lorenzo Steccanella, Simone Totaro, Damien Allonsius +1
Sparse-reward domains are challenging for reinforcement learning algorithms since significant exploration is needed before encountering reward for the first time. Hierarchical rein…
Lifelong Control of Off-grid Microgrid with Model Based Reinforcement Learning
Simone Totaro, Ioannis Boukas, Anders Jonsson +1
The lifelong control problem of an off-grid microgrid is composed of two tasks, namely estimation of the condition of the microgrid devices and operational planning accounting for…
Adaptive Smoothing Path Integral Control
Dominik Thalmeier, Hilbert J. Kappen, Simone Totaro +1
In Path Integral control problems a representation of an optimally controlled dynamical system can be formally computed and serve as a guidepost to learn a parametrized policy. The…
Recurrent Neural Networks with Flexible Gates using Kernel Activation Functions
Simone Scardapane, Steven Van Vaerenbergh, Danilo Comminiello +2
Gated recurrent neural networks have achieved remarkable results in the analysis of sequential data. Inside these networks, gates are used to control the flow of information, allow…