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Found 16 papers, 1 papers with code
An Online Spatial-Temporal Graph Trajectory Planner for Autonomous Vehicles
Among these modules, the trajectory planner plays a pivotal role in the safety of the vehicle and the comfort of its passengers.
Traffic Signal Control Using Lightweight Transformers: An Offline-to-Online RL Approach
In this work, we propose DTLight, a simple yet powerful lightweight Decision Transformer-based TSC method that can learn policy from easily accessible offline datasets.
Anomaly Detection with Ensemble of Encoder and Decoder
Usually, machine learning-based methods need to model the normal data distribution.
Adaptive Aggregation for Safety-Critical Control
Safety has been recognized as the central obstacle to preventing the use of reinforcement learning (RL) for real-world applications.
MetaEMS: A Meta Reinforcement Learning-based Control Framework for Building Energy Management System
The building sector has been recognized as one of the primary sectors for worldwide energy consumption.
Reinforcement Learning Based Dynamic Model Combination for Time Series Forecasting
To deal with this challenge, we propose a reinforcement learning (RL) based model combination (RLMC) framework for determining model weights in an ensemble for time series forecasting tasks.
Time Series Anomaly Detection via Reinforcement Learning-Based Model Selection
Time series anomaly detection has been recognized as of critical importance for the reliable and efficient operation of real-world systems.
An Early Fault Detection Method of Rotating Machines Based on Multiple Feature Fusion with Stacking Architecture
One of the key points of EFD is developing a generic model to extract robust and discriminative features from different equipment for early fault detection.
Meta-Learning Based Early Fault Detection for Rolling Bearings via Few-Shot Anomaly Detection
To address this problem, many transfer learning based EFD methods utilize historical data to learn transferable domain knowledge and conduct early fault detection on new target bearings.
Structured learning of safety guarantees for the control of uncertain dynamical systems
In this article, we propose the safe uncertainty learning principle, and argue that the learning must be properly structured to preserve safety guarantees.
Learning-based synthesis of robust linear time-invariant controllers
Recent advances in learning for control allow to synthesize vehicle controllers from learned system dynamics and maintain robust stability guarantees.
Improving gearshift controllers for electric vehicles with reinforcement learning
During a multi-speed transmission development process, the final calibration of the gearshift controller parameters is usually performed on a physical test bench.
ModelLight: Model-Based Meta-Reinforcement Learning for Traffic Signal Control
Traffic signal control is of critical importance for the effective use of transportation infrastructures.
Benchmarking Sample Selection Strategies for Batch Reinforcement Learning
Through extensive experiments on the standard batch RL datasets, we find that non-uniform sampling is also effective in batch RL settings.
Time Series Anomaly Detection for Smart Grids: A Survey
With the rapid increase in the integration of renewable energy generation and the wide adoption of various electric appliances, power grids are now faced with more and more challenges.
Data-driven Model Predictive and Reinforcement Learning Based Control for Building Energy Management: a Survey
Building energy management is one of the core problems in modern power grids to reduce energy consumption while ensuring occupants' comfort.
