7 citations · 11 across the 8 of their papers we have counts for
3 papers · 1 filter
Who is Afraid of Minimal Revision?
Edoardo Baccini, Zoé Christoff, Nina Gierasimczuk +1
The principle of minimal change in belief revision theory requires that, when accepting new information, one keeps one's belief state as close to the initial belief state as possib…
SymDQN: Symbolic Knowledge and Reasoning in Neural Network-based Reinforcement Learning
Ivo Amador, Nina Gierasimczuk
We propose a learning architecture that allows symbolic control and guidance in reinforcement learning with deep neural networks. We introduce SymDQN, a novel modular approach that…
Learning to Act and Observe in Partially Observable Domains
Thomas Bolander, Nina Gierasimczuk, Andrés Occhipinti Liberman
We consider a learning agent in a partially observable environment, with which the agent has never interacted before, and about which it learns both what it can observe and how its…