Steven Loscalzo

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We model an intelligence collection activity as multiobjective optimization on a binary stochastic physical search problem, providing formal definitions of the problem space and nondominated solution sets. We present the Iterative Domination Solver as an approximate method for generating solution sets that can be used by a human decision maker to meet the(More)
In many multi-agent applications, such as patrol, shopping, or mining, a group of agents must use limited resources to successfully accomplish a task possibly available at several distinct sites. We investigate problems where agents must expend resources (e.g. battery power) to both travel between sites and to accomplish the task at a site, and where agents(More)
Reinforcement learning (RL) is designed to learn optimal control policies from unsupervised interactions with the environment. Many successful RL algorithms have been developed, however, none of them can efficiently tackle problems with high-dimensional state spaces due to the "curse of dimensionality," and so their applicability to real-world scenarios is(More)
Approximate value iteration methods for reinforcement learning (RL) generalize experience from limited samples across large stateaction spaces. The function approximators used in such methods typically introduce errors in value estimation which can harm the quality of the learned value functions. We present a new batch-mode, off-policy, approximate value(More)
Appyling reinforcement learning techniques in continuous environments is challenging because there are infinitely many states to visit in order to learn an optimal policy. To make this situation tractable, abstractions are often used to reduce the infinite state space down to a small and finite one. Some of the more powerful and commonplace abstractions,(More)