Temporal difference methods are theoretically grounded and empirically effective methods for addressing reinforcement learning problems. In most real-world reinforcement learning ...
— This paper analyzes the potential to apply mobile service robots in offshore oil and gas producing environments. The required hardware and software components and abilities of ...
Matthias Bengel, Kai Pfeiffer, Birgit Graf, Alexan...
— The rapidly increasing complexity of tasks robotic systems are expected to carry out underscores the need for the development of motion planners that can take into account disc...
—The design of multi-channel multi-hop wireless mesh networks is centered around the way nodes synchronize when they need to communicate. However, existing designs are confined ...
Recent work has shown the promise in using local-search “probes” as a basis for directing a backtracking-based refinement search. In this approach, the decision about the next...
Alexander Nareyek, Stephen F. Smith, Christian M. ...