Approximate Model-Based Shielding for Safe Reinforcement Learning. (arXiv:2308.00707v1 [cs.LG])

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 easy as many algorithms are sample-inefficient and
maximising the standard RL objective comes with no guarantees on worst-case
performance. In this paper we propose approximate model-based shielding (AMBS),
a principled look-ahead shielding algorithm for verifying the performance of
learned RL policies w.r.t. a set of given safety constraints. Our algorithm
differs from other shielding approaches in that it does not require prior
knowledge of the safety-relevant dynamics of the system. We provide a strong
theoretical justification for AMBS and demonstrate superior performance to
other safety-aware approaches on a set of Atari games with state-dependent
safety-labels.