LEO: Liquid Exploration Online
نویسندگان
چکیده
منابع مشابه
Online Exploration of Rectangular Grids
In this paper, we consider the problem of exploring unknown environments with autonomous agents. We model the environment as a graph with edge weights and analyze the task of visiting all vertices of the graph at least once. The hardness of this task heavily depends on the knowledge and the capabilities of the agent. In our model, the agent sees the whole graph in advance, but does not know the...
متن کاملOnline Graph Exploration with Advice
We study the problem of exploring an unknown undirected graph with non-negative edge weights. Starting at a distinguished initial vertex s, an agent must visit every vertex of the graph and return to s. Upon visiting a node, the agent learns all incident edges, their weights and endpoints. The goal is to find a tour with minimal cost of traversed edges. This variant of the exploration problem h...
متن کاملParameter exploration of optically trapped liquid aerosols.
When studying the motion of optically trapped particles on the microsecond time scale, in low-viscosity media such as air, inertia cannot be neglected. Resolution of unusual and interesting behavior not seen in colloidal trapping experiments is possible. In an attempt to explain the phenomena we use power-spectral methods to perform a parameter study of the Brownian motion of optically trapped ...
متن کاملImage-Based Exploration of Massive Online Environments
This paper presents a system for interactive exploration of massive, detailed virtual environments over a broadband network. We build upon the hierarchical image-based framework pioneered by Shade et al. [1996] and Schaufler and Stürzlinger [1996], introducing key adaptations for scalability. A cluster of servers maintain a hierarchy of depth images of bounded regions of the scene. A client dis...
متن کاملOnline exploration in least-squares policy iteration
One of the key problems in reinforcement learning is balancing exploration and exploitation. Another is learning and acting in large or even continuous Markov decision processes (MDPs), where compact function approximation has to be used. In this paper, we provide a practical solution to exploring large MDPs by integrating a powerful exploration technique, Rmax, into a state-of-the-art learning...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Robotic Computing
سال: 2020
ISSN: 2641-9521
DOI: 10.35708/rc1869-126259