9 papers
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…
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…
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…
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…
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…
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…