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Found 4 papers, 3 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 or training data 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.
DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep Neural Networks
It reduces the cost of labeling by prioritizing the selection of test inputs with high fault revealing power from large unlabeled datasets.
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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