Search Results for author:
Found 8 papers, 0 papers with code
MetaCURL: Non-stationary Concave Utility Reinforcement Learning
Under partial information on the probability transitions (uncertainty and non-stationarity coming only from external noise, independent of agent state-action pairs), we achieve optimal dynamic regret without prior knowledge of MDP changes.
Automated Deep Learning for Load Forecasting
Accurate forecasting of electricity consumption is essential to ensure the performance and stability of the grid, especially as the use of renewable energy increases.
A Bandit Approach with Evolutionary Operators for Model Selection
The reward is the accuracy of the selected model after its partial training.
Efficient Model-Based Concave Utility Reinforcement Learning through Greedy Mirror Descent
Many machine learning tasks can be solved by minimizing a convex function of an occupancy measure over the policies that generate them.
Reimagining Demand-Side Management with Mean Field Learning
Integrating renewable energy into the power grid while balancing supply and demand is a complex issue, given its intermittent nature.
Simulating Tariff Impact in Electrical Energy Consumption Profiles with Conditional Variational Autoencoders
Another contribution is that the clustering approach segments consumers according to their daily consumption profile and elasticity to tariff changes.
Online Hierarchical Forecasting for Power Consumption Data
Our approach consists in three steps: feature generation, aggregation and projection.
Target Tracking for Contextual Bandits: Application to Demand Side Management
We propose a contextual-bandit approach for demand side management by offering price incentives.
