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Found 10 papers, 0 papers with code
K-nearest Multi-agent Deep Reinforcement Learning for Collaborative Tasks with a Variable Number of Agents
Traditionally, the performance of multi-agent deep reinforcement learning algorithms are demonstrated and validated in gaming environments where we often have a fixed number of agents.
Data-driven Residual Generation for Early Fault Detection with Limited Data
Model-based fault detection and isolation methods use system model to generate a set of residuals as the bases for fault detection and isolation.
An Offline Deep Reinforcement Learning for Maintenance Decision-Making
Several machine learning and deep learning frameworks have been proposed to solve remaining useful life estimation and failure prediction problems in recent years.
Deep Reinforcement Learning with Adjustments
Our method can learn complex policies to achieve long-term goals and at the same time it can be easily adjusted to address short-term requirements without retraining.
A Non-linear Function-on-Function Model for Regression with Time Series Data
Specifically, we aim to learn mathematical mappings from multiple chronologically measured numerical variables within a certain time interval S to multiple numerical variables of interest over time interval T. Prior arts, including the multivariate regression model, the Seq2Seq model, and the functional linear models, suffer from several limitations.
Challenges of Applying Deep Reinforcement Learning in Dynamic Dispatching
In this paper, we review the main challenges in using deep RL to address the dynamic dispatching problem in the mining industry.
Spatio-Temporal Functional Neural Networks
Explosive growth in spatio-temporal data and its wide range of applications have attracted increasing interests of researchers in the statistical and machine learning fields.
Dynamic Dispatching for Large-Scale Heterogeneous Fleet via Multi-agent Deep Reinforcement Learning
In this paper, we propose a novel Deep Reinforcement Learning approach to solve the dynamic dispatching problem in mining.
Comparison of Model Predictive and Reinforcement Learning Methods for Fault Tolerant Control
A desirable property in fault-tolerant controllers is adaptability to system changes as they evolve during systems operations.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Long-term planning, short-term adjustments
Our method can learn complex policies to achieve long-term goals and at the same time it can be easily adjusted to address short-term requirements without retraining.
