Reducing Safety Interventions in Provably Safe Reinforcement Learning
Jakob Thumm,Guillaume Pelat,Matthias Althoff,Jakob Thumm,Guillaume Pelat,Matthias Althoff
Deep Reinforcement Learning (RL) has shown promise in addressing complex robotic challenges. In real-world applications, RL is often accompanied by failsafe controllers as a last resort to avoid catastrophic events. While necessary for safety, these interventions can result in undesirable behaviors, such as abrupt braking or aggressive steering. This paper proposes two safety intervention reductio...
