Adapting Surprise Minimizing Reinforcement Learning Techniques for Transactive Control
This work addresses energy management for grid operators, but appears incremental as it adapts existing techniques to a specific domain.
The authors tackled the problem of optimizing energy demand response pricing by proposing a reinforcement learning controller with surprise minimizing modifications, which performed well in simulation.
Optimizing prices for energy demand response requires a flexible controller with ability to navigate complex environments. We propose a reinforcement learning controller with surprise minimizing modifications in its architecture. We suggest that surprise minimization can be used to improve learning speed, taking advantage of predictability in peoples' energy usage. Our architecture performs well in a simulation of energy demand response. We propose this modification to improve functionality and save in a large scale experiment.