Planning in Decentralized POMDPs with Predictive Policy Representations

We discuss the problem of policy representation in stochastic and partially observable systems, and address the case where the policy is a hidden parameter of the planning problem. We propose an adaptation of the Predictive State Representations (PSRs) to this problem by introducing tests (sequences of actions and observations) on policies. The new model, called the Predictive Policy Representations (PPRs), is potentially more compact than the other representations, such as decision trees or Finite-State Controllers (FSCs). In this paper, we show how PPRs can be used to improve the performances of a point-based algorithm for DEC-POMDP

MetaCitta: Deep Meta-Learning for Spatio-Temporal Prediction Across Cities and Tasks

Accurate spatio-temporal prediction is essential for capturing city dynamics and planning mobility services. State-of-the-art deep spatio-temporal predictive models depend on rich and representative training data for target regions and tasks. However, the availability of such data is typically limited. Furthermore, existing predictive models fail to utilize cross-correlations across tasks and cities. In this paper, we propose MetaCitta, a novel deep meta-learning approach that addresses the critical challenges of data scarcity and model generalization. MetaCitta adopts the data from different cities and tasks in a generalizable spatio-temporal deep neural network. We propose a novel meta-learning algorithm that minimizes the discrepancy between spatio-temporal representations across tasks and cities. Our experiments with real-world data demonstrate that the proposed MetaCitta approach outperforms state-of-the-art prediction methods for zero-shot learning and pre-training plus fine-tuning. Furthermore, MetaCitta is computationally more efficient than the existing meta-learning approaches