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Found 11 papers, 0 papers with code
Large Language Models Powered Context-aware Motion Prediction
Traditional methods of motion forecasting primarily encode vector information of maps and historical trajectory data of traffic participants, lacking a comprehensive understanding of overall traffic semantics, which in turn affects the performance of prediction tasks.
Anomaly Detection via Learning-Based Sequential Controlled Sensing
Our objective is to design a sequential selection policy that dynamically determines which processes to observe at each time with the goal to minimize the delay in making the decision and the total sensing cost.
A Predictive Online Transient Stability Assessment with Hierarchical Generative Adversarial Networks
In this paper, a new data-driven TSA approach is developed for TSA with fewer data compared to the conventional methods.
Scalable and Decentralized Algorithms for Anomaly Detection via Learning-Based Controlled Sensing
In this setting, we develop an anomaly detection algorithm that chooses the processes to be observed at a given time instant, decides when to stop taking observations, and declares the decision on anomalous processes.
Anomaly Detection via Controlled Sensing and Deep Active Inference
In this paper, we address the anomaly detection problem where the objective is to find the anomalous processes among a given set of processes.
Anomaly Detection and Sampling Cost Control via Hierarchical GANs
In order to improve the detection accuracy and reduce the delay in detection, we introduce a buffer zone in the operation of the proposed GAN-based detector.
Adversarial jamming attacks and defense strategies via adaptive deep reinforcement learning
As the applications of deep reinforcement learning (DRL) in wireless communications grow, sensitivity of DRL based wireless communication strategies against adversarial attacks has started to draw increasing attention.
Deep Actor-Critic Reinforcement Learning for Anomaly Detection
Anomaly detection is widely applied in a variety of domains, involving for instance, smart home systems, network traffic monitoring, IoT applications and sensor networks.
A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access
We consider both a single-user case and a scenario in which multiple users attempt to access channels simultaneously.
Deep Multi-Agent Reinforcement Learning Based Cooperative Edge Caching in Wireless Networks
The growing demand on high-quality and low-latency multimedia services has led to much interest in edge caching techniques.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Actor-Critic Deep Reinforcement Learning for Dynamic Multichannel Access
We consider the dynamic multichannel access problem, which can be formulated as a partially observable Markov decision process (POMDP).
