3 citations · 5 across the 7 of their papers we have counts for
4 papers · 1 filter
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…
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…