Designing custom solutions has been central to meeting a range of stringent and specialized needs of embedded computing, along such dimensions as physical size, power consumption, ...
Krishna V. Palem, Lakshmi N. Chakrapani, Sudhakar ...
We propose a model-based learning algorithm, the Adaptive Aggregation Algorithm (AAA), that aims to solve the online, continuous state space reinforcement learning problem in a de...
—The actor programming model offers a promising model for developing reliable parallel and distributed code. Actors provide flexibility and scalability: local execution may be i...
Steven Lauterburg, Mirco Dotta, Darko Marinov, Gul...
In this paper, we present a framework for categorical data analysis which allows such data sets to be explored using a rich set of techniques that are only applicable to continuou...
Abstract. We present a new reinforcement learning approach for deterministic continuous control problems in environments with unknown, arbitrary reward functions. The difficulty of...