Autoregressive generative models can estimate complex continuous data
distributions, like trajectory rollouts in an RL environment, image
intensities, and audio. Most state-of-the-art models discretize continuous data
into several bins and use categorical distributions over the bins to
approximate the continuous data distribution. The advantage is that the
categorical distribution can easily express multiple modes and are
straightforward to optimize. However, such approximation cannot express sharp
changes in density without using significantly more bins, making it parameter
inefficient. We propose an efficient, expressive, multimodal parameterization
called Adaptive Categorical Discretization (AdaCat). AdaCat discretizes each
dimension of an autoregressive model adaptively, which allows the model to
allocate density to fine intervals of interest, improving parameter efficiency.
AdaCat generalizes both categoricals and quantile-based regression. AdaCat is a
simple add-on to any discretization-based distribution estimator. In
experiments, AdaCat improves density estimation for real-world tabular data,
images, audio, and trajectories, and improves planning in model-based offline
RL.

通过提出一种自适应的分类离散化方法，优化自回归生成模型中的连续数据分布，提高参数效率，并在现实世界的数据、图像、音频和轨迹中改善密度估计，同时改进了基于模型的离线强化学习中的规划。