Using Meta Reinforcement Learning to Bridge the Gap between Simulation and Experiment in Energy Demand Response.
Doseok JangLucas SpangherManan KhattarUtkarsha AgwanCostas J. SpanosPublished in: e-Energy (2021)
Keyphrases
- reinforcement learning
- function approximation
- multi agent
- numerical simulations
- genetic algorithm
- state space
- energy consumption
- simulation model
- reinforcement learning algorithms
- optimal control
- total energy
- distribution network
- energy minimization
- multi agent reinforcement learning
- transition model
- wind farm
- renewable energy
- robocup soccer
- real robot
- neural network
- mathematical model
- markov random field
- dynamic programming
- learning process
- machine learning