2 research outputs found
Algorithmic Design and Implementation of Unobtrusive Multistatic Serial LiDAR Image
To fully understand interactions between marine hydrokinetic (MHK) equipment
and marine animals, a fast and effective monitoring system is required to
capture relevant information whenever underwater animals appear. A new
automated underwater imaging system composed of LiDAR (Light Detection and
Ranging) imaging hardware and a scene understanding software module named
Unobtrusive Multistatic Serial LiDAR Imager (UMSLI) to supervise the presence
of animals near turbines. UMSLI integrates the front end LiDAR hardware and a
series of software modules to achieve image preprocessing, detection, tracking,
segmentation and classification in a hierarchical manner
Guided Policy Exploration for Markov Decision Processes using an Uncertainty-Based Value-of-Information Criterion
Reinforcement learning in environments with many action-state pairs is
challenging. At issue is the number of episodes needed to thoroughly search the
policy space. Most conventional heuristics address this search problem in a
stochastic manner. This can leave large portions of the policy space unvisited
during the early training stages. In this paper, we propose an
uncertainty-based, information-theoretic approach for performing guided
stochastic searches that more effectively cover the policy space. Our approach
is based on the value of information, a criterion that provides the optimal
trade-off between expected costs and the granularity of the search process. The
value of information yields a stochastic routine for choosing actions during
learning that can explore the policy space in a coarse to fine manner. We
augment this criterion with a state-transition uncertainty factor, which guides
the search process into previously unexplored regions of the policy space.Comment: IEEE Transactions on Neural Networks and Learning System