3 citations · 3 across the 13 of their papers we have counts for
5 papers · 1 filter
Verifier-Guided Model Discovery for Physical Dynamical Systems with Pretrained Symbolic Transformers
Farbod Faraji, Francesco Belardinelli
Reliable forecasting of nonlinear physical systems underpins scientific discovery and engineering decision-making. Yet high-fidelity simulations are prohibitively costly, and machi…
Safe Reinforcement Learning via Recovery-based Shielding with Gaussian Process Dynamics Models
Alexander W. Goodall, Francesco Belardinelli
Reinforcement learning (RL) is a powerful framework for optimal decision-making and control but often lacks provable guarantees for safety-critical applications. In this paper, we…
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…
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…
Leveraging Approximate Model-based Shielding for Probabilistic Safety Guarantees in Continuous Environments
Alexander W. Goodall, Francesco Belardinelli
Shielding is a popular technique for achieving safe reinforcement learning (RL). However, classical shielding approaches come with quite restrictive assumptions making them difficu…