We additionally record a new RGB-Infra indoor dataset, namely L515-Indoors, and demonstrate that the proposed object detection methodologies are highly effective for a variety of lighting conditions.
This paper presents DeepKoCo, a novel model-based agent that learns a latent Koopman representation from images.
A wellknown batch reinforcement learning technique, fitted Q-iteration, is used to find a control policy, given this feature representation.
We propose a model-free Monte-Carlo estimator method that uses a metric to construct artificial trajectories and we illustrate this method by finding the day-ahead schedule of a heat-pump thermostat.
This article deals with stochastic processes endowed with the Markov (memoryless) property and evolving over general (uncountable) state spaces.
The parameters of the control law are found using actor-critic reinforcement learning, enabling learning near-optimal control policies satisfying a desired closed-loop energy landscape.