SafeLife 1.0: Exploring Side Effects in Complex Environments

We present SafeLife, a publicly available reinforcement learning environment that tests the safety of reinforcement learning agents. It contains complex, dynamic, tunable, procedurally generated levels with many opportunities for unsafe behavior. Agents are graded both on their ability to maximize their explicit reward and on their ability to operate safely without unnecessary side effects. We train agents to maximize rewards using proximal policy optimization and score them on a suite of benchmark levels. The resulting agents are performant but not safe -- they tend to cause large side effects in their environments -- but they form a baseline against which future safety research can be measured.
Comment: Updated version was presented at the AAAI SafeAI 2020 Workshop, but now with updated contact info. Previously presented at the 2019 NeurIPS Safety and Robustness in Decision Making Workshop