17 citations · 17 across the 1 of their papers we have counts for
3 papers · 1 filter
SAMBA: Safe Model-Based & Active Reinforcement Learning
Alexander I. Cowen-Rivers, Daniel Palenicek, Vincent Moens +4
In this paper, we propose SAMBA, a novel framework for safe reinforcement learning that combines aspects from probabilistic modelling, information theory, and statistics. Our metho…
Wasserstein Robust Reinforcement Learning
Mohammed Amin Abdullah, Hang Ren, Haitham Bou Ammar +4
Reinforcement learning algorithms, though successful, tend to over-fit to training environments hampering their application to the real-world. This paper proposes $\text{W}\text{R}…
Reinforcement Learning with Wasserstein Distance Regularisation, with Applications to Multipolicy Learning
Mohammed Amin Abdullah, Aldo Pacchiano, Moez Draief
We describe an application of Wasserstein distance to Reinforcement Learning. The Wasserstein distance in question is between the distribution of mappings of trajectories of a poli…