  Recent advances in reinforcement learning have inspired increasing interest
in learning user modeling adaptively through dynamic interactions, e.g., in
reinforcement learning based recommender systems. Reward function is crucial
for most of reinforcement learning applications as it can provide the guideline
about the optimization. However, current reinforcement-learning-based methods
rely on manually-defined reward functions, which cannot adapt to dynamic and
noisy environments. Besides, they generally use task-specific reward functions
that sacrifice generalization ability. We propose a generative inverse
reinforcement learning for user behavioral preference modelling, to address the
above issues. Instead of using predefined reward functions, our model can
automatically learn the rewards from user's actions based on discriminative
actor-critic network and Wasserstein GAN. Our model provides a general way of
characterizing and explaining underlying behavioral tendencies, and our
experiments show our method outperforms state-of-the-art methods in a variety
of scenarios, namely traffic signal control, online recommender systems, and
scanpath prediction.
