2023
DOI: 10.48550/arxiv.2303.03339
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Reducing Safety Interventions in Provably Safe Reinforcement Learning

Abstract: 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 reduction methods: action replacement and projection, which change the agent's action if it leads to an unsafe state. These… Show more

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