3 citations · 5 across the 7 of their papers we have counts for
7 papers
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…
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…
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…
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…
Approximate Model-Based Shielding for Safe Reinforcement Learning
Alexander W. Goodall, Francesco Belardinelli
Reinforcement learning (RL) has shown great potential for solving complex tasks in a variety of domains. However, applying RL to safety-critical systems in the real-world is not ea…
Approximate Shielding of Atari Agents for Safe Exploration
Alexander W. Goodall, Francesco Belardinelli
Balancing exploration and conservatism in the constrained setting is an important problem if we are to use reinforcement learning for meaningful tasks in the real world. In this pa…