Abstract
Reinforcement Learning (RL) algorithms have achieved remarkable performance in decision making and control tasks due to their ability to reason about long-term, cumulative reward using trial and error. However, during RL training, applying this trial-and-error approach to real-world robots operating in safety critical environment may lead to collisions. To address this challenge, this paper proposes a Reachability-based Trajectory Safeguard (RTS), which leverages trajectory parameterization and reachability analysis to ensure safety while a policy is being learned. This method ensures a robot with continuous action space can be trained from scratch safely in real-time. Importantly, this safety layer can still be applied after a policy has been learned. The efficacy of this method is illustrated on three nonlinear robot models, including a 12-D quadrotor drone, in simulation. By ensuring safety with RTS, this paper demonstrates that the proposed algorithm is not only safe, but can achieve a higher reward in a considerably shorter training time when compared to a non-safe counterpart.
Abstract (translated)
URL
https://arxiv.org/abs/2011.08421
