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Found 6 papers, 0 papers with code
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.
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.
Regularized Operating Envelope with Interpretability and Implementability Constraints
However, a bigger challenge with these approaches is that they don't take into account two key features that are needed to operationalize operating envelopes: (i) interpretability of the envelope by the operator and (ii) implementability of the envelope from a practical standpoint.
Manufacturing Dispatching using Reinforcement and Transfer Learning
To address this, we enhance our deep RL model with an approach for dispatching policy transfer.
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.
Remaining Useful Life Estimation Using Functional Data Analysis
In this work, we propose a novel Functional Data Analysis (FDA) method called functional Multilayer Perceptron (functional MLP) for RUL estimation.
