3 citations · 4 across the 13 of their papers we have counts for
4 papers · 1 filter
About Time: Model-free Reinforcement Learning with Timed Reward Machines
Rajarshi Roy, Anirban Majumdar, Ritam Raha +2
Reward specification plays a central role in reinforcement learning (RL), guiding the agent's behavior. To express non-Markovian rewards, formalisms such as reward machines have be…
Probabilistic Model Checking: Applications and Trends
Marta Kwiatkowska, Gethin Norman, David Parker
Probabilistic model checking is an approach to the formal modelling and analysis of stochastic systems. Over the past twenty five years, the number of different formalisms and tech…
Learning Probabilistic Temporal Logic Specifications for Stochastic Systems
Rajarshi Roy, Yash Pote, David Parker +1
There has been substantial progress in the inference of formal behavioural specifications from sample trajectories, for example, using Linear Temporal Logic (LTL). However, these t…
Planning with Linear Temporal Logic Specifications: Handling Quantifiable and Unquantifiable Uncertainty
Pian Yu, Yong Li, David Parker +1
This work studies the planning problem for robotic systems under both quantifiable and unquantifiable uncertainty. The objective is to enable the robotic systems to optimally fulfi…