SafeLife 1.0: Exploring Side Effects in Complex Environments

Understand

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.