3 citations · 3 across the 13 of their papers we have counts for
6 papers · 1 filter
Synthesis of Safety Specifications for Probabilistic Systems
Gaspard Ohlmann, Edwin Hamel-De le Court, Francesco Belardinelli
Ensuring that agents satisfy safety specifications can be crucial in safety-critical environments. While methods exist for controller synthesis with safe temporal specifications, m…
Behaviour Policy Optimization: Provably Lower Variance Return Estimates for Off-Policy Reinforcement Learning
Alexander W. Goodall, Edwin Hamel-De le Court, Francesco Belardinelli
Many reinforcement learning algorithms, particularly those that rely on return estimates for policy improvement, can suffer from poor sample efficiency and training instability due…
Adaptive GR(1) Specification Repair for Liveness-Preserving Shielding in Reinforcement Learning
Tiberiu-Andrei Georgescu, Alexander W. Goodall, Dalal Alrajeh +2
Shielding is widely used to enforce safety in reinforcement learning (RL), ensuring that an agent's actions remain compliant with formal specifications. Classical shielding approac…
ProSh: Probabilistic Shielding for Model-free Reinforcement Learning
Edwin Hamel-De le Court, Gaspard Ohlmann, Francesco Belardinelli
Safety is a major concern in reinforcement learning (RL): we aim at developing RL systems that not only perform optimally, but are also safe to deploy by providing formal guarantee…
Probabilistic Shielding for Safe Reinforcement Learning
Edwin Hamel-De le Court, Francesco Belardinelli, Alexander W. Goodall
In real-life scenarios, a Reinforcement Learning (RL) agent aiming to maximise their reward, must often also behave in a safe manner, including at training time. Thus, much attenti…
Convergence and Connectivity: Dynamics of Multi-Agent Q-Learning in Random Networks
Dan Leonte, Aamal Hussain, Raphael Huser +2
Beyond specific settings, many multi-agent learning algorithms fail to converge to an equilibrium solution, instead displaying complex, non-stationary behaviours such as recurrent…