14 citations · 54 across the 21 of their papers we have counts for
3 papers · 1 filter
Identification of Unexpected Decisions in Partially Observable Monte-Carlo Planning: a Rule-Based Approach
Giulio Mazzi, Alberto Castellini, Alessandro Farinelli
Partially Observable Monte-Carlo Planning (POMCP) is a powerful online algorithm able to generate approximate policies for large Partially Observable Markov Decision Processes. The…
Evaluating the Safety of Deep Reinforcement Learning Models using Semi-Formal Verification
Davide Corsi, Enrico Marchesini, Alessandro Farinelli
Groundbreaking successes have been achieved by Deep Reinforcement Learning (DRL) in solving practical decision-making problems. Robotics, in particular, can involve high-cost hardw…
POMP: Pomcp-based Online Motion Planning for active visual search in indoor environments
Yiming Wang, Francesco Giuliari, Riccardo Berra +5
In this paper we focus on the problem of learning an optimal policy for Active Visual Search (AVS) of objects in known indoor environments with an online setup. Our POMP method use…