DreamTest: World-Model Surrogates for Search-Based Testing of Deep Reinforcement Learning Agents

Testing deep reinforcement learning (DRL) agents in cyber-physical systems aims to uncover diverse failures before deployment, but each execution can be expensive. Surrogate-assisted testing reduces this cost by learning to predict which test configurations are likely to fail. Prior surrogates treat the system as a black box and predict pass or fail outcomes directly; we instead model how a test unfolds and estimate failure from an imagined episode. We introduce DreamTest, a world-model surrogate for testing DRL agents. DreamTest adapts a recurrent state-space model to learn agent behaviour and environment dynamics from the agent's training log. Given a candidate configuration, imagined rollouts produce a failure score that guides search without executing every candidate in a simulator or real system. We evaluate DreamTest for failure prediction, test generation, and failure diversity on Parking, Humanoid, and DonkeyCar. Mean area under the precision-recall curve (AUPRC) exceeds the strongest baseline by 97%, 12%, and 39%, respectively, and gains on five out-of-distribution test sets reach 145%, 29%, and 44%. Under the same simulator-validation budget, the best "DreamTest + search" combinations find 29%, 22%, and 79% more novel failures on average. Across clusterings with k = 2-40, failures generated with DreamTest cover the most behavioural clusters for almost all k, indicating that DreamTest consistently discovers behaviourally diverse failures.