This paper investigates decentralized motion planning for multi-agent exploration in a field with unknown received signal strength (RSS) distribution.
Sample applications include agents in exploration and patrol missions (e.g., an agent seeking to find the best location to deploy sensing equipment along its path).
By using a probabilistic plan selection function, the agents can balance exploration and exploitation of their plans.
Application Case 2 Emerging application scenarios for miniature wireless agents include the exploration of secluded and difficult-to-access environments such as subterranean cavities or pipelines.
Then, Q-learning with a cooperative swarm is adopted for the knowledge learning for each agent via appropriate explorations and exploitations in an unknown environment.
BRCA1 and the basal phenotype may be valuable predictive biomarkers of response to DNA damaging agents, but further exploration in larger prospective clinical trials is required.
These conditions limit the use of rats for assessment of new treatments for osteoporosis, especially bone-forming agents, which require exploration of dynamic trabecular and cortical bone remodeling.
In the first step, a genetic algorithm is applied by a scheduler agent for a global exploration of the search space.
First, a neighborhood-based genetic algorithm (NGA) is applied by a scheduler agent for a global exploration of the search space.
But the very earthly cat-and-mouse game between smugglers and America's border agents is affecting the exploration of space, lighting up the nighttime sky in southern Arizona and making astronomers strain even harder to figure out the mysteries of the universe.
The learning process happens at regular time steps and is basically a signal for the agents to start an exploration phase.
