Detecting Anomalous Agent Decision Sequences Based on Offline Imitation Learning

Anomaly detection in decision-making sequences is a challenging problem due to the complexity of normality representation learning, the sequential nature of the task and the difficulty of realworld implementation. In this work, we propose extracting two behaviour features: action optimality and sequential association to detect anomalous behaviour. Our offline imitation learning model is an adaptation of behavioural cloning with a transformer policy network, where we modify the training process to learn a Q function and a state value function from normal trajectories.