9 citations · 19 across the 9 of their papers we have counts for
10 papers
Safe Reinforcement Learning via Probabilistic Logic Shields
Wen-Chi Yang, Giuseppe Marra, Gavin Rens +1
Safe Reinforcement learning (Safe RL) aims at learning optimal policies while staying safe. A popular solution to Safe RL is shielding, which uses a logical safety specification to…
Learning Probabilistic Temporal Safety Properties from Examples in Relational Domains
Gavin Rens, Wen-Chi Yang, Jean-François Raskin +1
We propose a framework for learning a fragment of probabilistic computation tree logic (pCTL) formulae from a set of states that are labeled as safe or unsafe. We work in a relatio…
Online Learning of Non-Markovian Reward Models
Gavin Rens, Jean-François Raskin, Raphaël Reynouad +1
There are situations in which an agent should receive rewards only after having accomplished a series of previous tasks, that is, rewards are non-Markovian. One natural and quite g…
Reputation-driven Decision-making in Networks of Stochastic Agents
David Maoujoud, Gavin Rens
This paper studies multi-agent systems that involve networks of self-interested agents. We propose a Markov Decision Process-derived framework, called RepNet-MDP, tailored to domai…
Learning Non-Markovian Reward Models in MDPs
Gavin Rens, Jean-François Raskin
There are situations in which an agent should receive rewards only after having accomplished a series of previous tasks. In other words, the reward that the agent receives is non-M…
Maximizing Expected Impact in an Agent Reputation Network -- Technical Report
Gavin Rens, Abhaya Nayak, Thomas Meyer
Many multi-agent systems (MASs) are situated in stochastic environments. Some such systems that are based on the partially observable Markov decision process (POMDP) do not take th…