To Tolerate or To Impute Missing Values in V2X Communications Data?

Misbehavior detection is a critical task in vehicular ad hoc networks. However, state-of-the-art data-driven techniques for misbehavior detection are usually conducted through complete V2X communication data collected from simulated experiments. This thesis evaluates the main strategies for the treatment of missing values to find out the best match for misbehavior detection with incomplete V2X communication data. This thesis proposes three novel methods for imputing and tolerating missing data. The first two are novel imputation methods that are based on cooperative clustering and collaborative clustering. The latter is a missing-tolerant method that is an ensemble learning based on the random subspace selection and Dempster-Shafer fusion. The effectiveness of the proposed techniques is evaluated in the ground truth vehicular reference misbehavior data. Moreover, a multi-factor amputation framework has been developed to induce missingness over V2X communication data with different missing ratios, mechanisms, and distributions. This framework provides a comprehensive benchmark resembling missingness over V2X communication data. The proposed methods are compared with some missing-tolerant and imputation methods. The attained results over benchmark data are analyzed and indicated the winner treatments in each aspect

SDRL: Interpretable and Data-efficient Deep Reinforcement Learning Leveraging Symbolic Planning

Deep reinforcement learning (DRL) has gained great success by learning directly from high-dimensional sensory inputs, yet is notorious for the lack of interpretability. Interpretability of the subtasks is critical in hierarchical decision-making as it increases the transparency of black-box-style DRL approach and helps the RL practitioners to understand the high-level behavior of the system better. In this paper, we introduce symbolic planning into DRL and propose a framework of Symbolic Deep Reinforcement Learning (SDRL) that can handle both high-dimensional sensory inputs and symbolic planning. The task-level interpretability is enabled by relating symbolic actions to options.This framework features a planner -- controller -- meta-controller architecture, which takes charge of subtask scheduling, data-driven subtask learning, and subtask evaluation, respectively. The three components cross-fertilize each other and eventually converge to an optimal symbolic plan along with the learned subtasks, bringing together the advantages of long-term planning capability with symbolic knowledge and end-to-end reinforcement learning directly from a high-dimensional sensory input. Experimental results validate the interpretability of subtasks, along with improved data efficiency compared with state-of-the-art approaches

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Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135493/1/srin05619.pd