Hawkes-Augmented World Model for Event-Driven Reinforcement Learning

World models enable reinforcement learning agents to improve policies through imagined latent trajectories. In environments with temporally clustered events, recent event history can provide useful context for predicting subsequent dynamics and rewards. We introduce the Hawkes-Augmented World Model (HAWM), which equips a recurrent stochastic state-space model with learned event inference and a Hawkes-style occurrence prior. An observation-conditioned posterior infers latent event occurrences and types from observed transitions, while a latent-conditioned prior predicts them during imagination. Both pathways share an exponentially decaying memory of binary event occurrences. The prior combines this memory with latent context to predict subsequent occurrence probabilities, while event types are predicted separately to condition the world model's prediction heads. The event-memory embedding also conditions recurrent dynamics and actor-critic learning, allowing accumulated event history to inform prediction and control. An auxiliary future-reward objective encourages the event representation to capture information relevant to subsequent rewards. HAWM thus introduces an explicit event-history state whose evolution can be inferred from observations and predicted during observation-free imagination.