Do androids dream of electric fences? Safety-aware reinforcement learning with latent shielding

Abstract

The growing trend of fledgling reinforcement learning sys- tems making their way into real-world applications has been accompanied by growing concerns for their safety and ro- bustness. In recent years, a variety of approaches have been put forward to address the challenges of safety-aware rein- forcement learning; however, these methods often either re- quire a handcrafted model of the environment to be pro- vided beforehand, or that the environment is relatively simple and low-dimensional. We present a novel approach to safety- aware deep reinforcement learning in high-dimensional envi- ronments called latent shielding. Latent shielding leverages internal representations of the environment learnt by model- based agents to “imagine” future trajectories and avoid those deemed unsafe. We experimentally demonstrate that this approach leads to improved adherence to formally-defined safety specifications