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Found 5 papers, 1 papers with code
Towards Personalization of User Preferences in Partially Observable Smart Home Environments
In this paper, we propose a Bayesian Reinforcement learning framework that can approximate the current occupant state in a partially observable smart home environment using its thermal preference, and then identify the occupant as a new user or someone is already known to the system.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Interval Timing: Modeling the break-run-break pattern using start/stop threshold-less drift-diffusion model
It is commonly assumed that in order to achieve the statistics with a pacemaker accumulator model it is necessary to have start and stop thresholds.
Latent Time-Adaptive Drift-Diffusion Model
Animals can quickly learn the timing of events with fixed intervals and their rate of acquisition does not depend on the length of the interval.
Potential Impacts of Smart Homes on Human Behavior: A Reinforcement Learning Approach
We then investigate the possibility of human behavior being altered as a result of the smart home and the human model adapting to one-another.
Adaptive Drift-Diffusion Process to Learn Time Intervals
We provide an analytical proof that the model can learn inter-event intervals in a number of trials independent of the interval size and that the temporal precision of the system is proportional to the timed interval.
