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Found 3 papers, 2 papers with code
SMARLA: A Safety Monitoring Approach for Deep Reinforcement Learning Agents
For practical reasons, SMARLA is designed to be black-box (as it does not require access to the internals of the agent) and leverages state abstraction to reduce the state space and thus facilitate the learning of safety violation prediction models from agent's states.
A Search-Based Testing Approach for Deep Reinforcement Learning Agents
However, such attacks often lead to unrealistic states of the environment.
Black-Box Testing of Deep Neural Networks Through Test Case Diversity
In this paper, we investigate black-box input diversity metrics as an alternative to white-box coverage criteria.
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