39 citations · 57 across the 17 of their papers we have counts for
6 papers · 1 filter
Safety Guarantees for Planning Based on Iterative Gaussian Processes
Kyriakos Polymenakos, Luca Laurenti, Andrea Patane +5
Gaussian Processes (GPs) are widely employed in control and learning because of their principled treatment of uncertainty. However, tracking uncertainty for iterative, multi-step p…
DeepSynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning
Mohammadhosein Hasanbeig, Natasha Yogananda Jeppu, Alessandro Abate +2
This paper proposes DeepSynth, a method for effective training of deep Reinforcement Learning (RL) agents when the reward is sparse and non-Markovian, but at the same time progress…
Verifying Reachability Properties in Markov Chains via Incremental Induction
Elizabeth Polgreen, Martin Brain, Martin Fraenzle +1
There is a scalability gap between probabilistic and non-probabilistic verification. Probabilistic model checking tools are based either on explicit engines or on (Multi-Terminal)…
Reinforcement Learning for Temporal Logic Control Synthesis with Probabilistic Satisfaction Guarantees
Mohammadhosein Hasanbeig, Yiannis Kantaros, Alessandro Abate +3
Reinforcement Learning (RL) has emerged as an efficient method of choice for solving complex sequential decision making problems in automatic control, computer science, economics,…
Modular Deep Reinforcement Learning with Temporal Logic Specifications
Lim Zun Yuan, Mohammadhosein Hasanbeig, Alessandro Abate +1
We propose an actor-critic, model-free, and online Reinforcement Learning (RL) framework for continuous-state continuous-action Markov Decision Processes (MDPs) when the reward is…
Bounded Model Checking of Max-Plus Linear Systems via Predicate Abstractions
Muhammad Syifa'ul Mufid, Dieky Adzkiya, Alessandro Abate
This paper introduces the abstraction of max-plus linear (MPL) systems via predicates. Predicates are automatically selected from system matrix, as well as from the specifications…