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Found 5 papers, 2 papers with code
Multi-Agent Reinforcement Learning for Fast-Timescale Demand Response of Residential Loads
To integrate high amounts of renewable energy resources, electrical power grids must be able to cope with high amplitude, fast timescale variations in power generation.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Sample Efficient Deep Reinforcement Learning via Uncertainty Estimation
In model-free deep reinforcement learning (RL) algorithms, using noisy value estimates to supervise policy evaluation and optimization is detrimental to the sample efficiency.
Batch Inverse-Variance Weighting: Deep Heteroscedastic Regression
In many situations however, the labelling process is able to estimate the variance of such distribution for each label, which can be used as an additional information to mitigate this impact.
Batch Inverse-Variance Weighting: Deep Heteroscedastic Regression using Privileged Information
In this work, we consider this setting and additionally assume that the label generating process is able to provide us with a quantity for the role of each label in the misalignment between the datasets, which we consider to be privileged information.
Deep Active Localization
Traditional approaches to this use an information-theoretic criterion for action selection and hand-crafted perceptual models.
