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SafeLife 1.0: Exploring Side Effects in Complex Environments
attributed to: Carroll L. Wainwright, Peter Eckersley
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... (Full Abstract in Full Plan- click title to view)
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Vulnerabilities & Strengths