7 research outputs found
Talk More Listen Less: Energy-Efficient Neighbor Discovery in Wireless Sensor Networks
Neighbor discovery is a fundamental service for initialization and managing
network dynamics in wireless sensor networks and mobile sensing applications.
In this paper, we present a novel design principle named Talk More Listen Less
(TMLL) to reduce idle-listening in neighbor discovery protocols by learning the
fact that more beacons lead to fewer wakeups. We propose an extended neighbor
discovery model for analyzing wakeup schedules in which beacons are not
necessarily placed in the wakeup slots. Furthermore, we are the first to
consider channel occupancy rate in discovery protocols by introducing a new
metric to trade off among duty-cycle, latency and channel occupancy rate.
Guided by the TMLL principle, we have designed Nihao, a family of
energy-efficient asynchronous neighbor discovery protocols for symmetric and
asymmetric cases. We compared Nihao with existing state of the art protocols
via analysis and real-world testbed experiments. The result shows that Nihao
significantly outperforms the others both in theory and practice.Comment: 9 pages, 14 figures, published in IEEE INFOCOM 201
Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value Regularization
Offline reinforcement learning (RL) has received considerable attention in
recent years due to its attractive capability of learning policies from offline
datasets without environmental interactions. Despite some success in the
single-agent setting, offline multi-agent RL (MARL) remains to be a challenge.
The large joint state-action space and the coupled multi-agent behaviors pose
extra complexities for offline policy optimization. Most existing offline MARL
studies simply apply offline data-related regularizations on individual agents,
without fully considering the multi-agent system at the global level. In this
work, we present OMIGA, a new offline m ulti-agent RL algorithm with implicit
global-to-local v alue regularization. OMIGA provides a principled framework to
convert global-level value regularization into equivalent implicit local value
regularizations and simultaneously enables in-sample learning, thus elegantly
bridging multi-agent value decomposition and policy learning with offline
regularizations. Based on comprehensive experiments on the offline multi-agent
MuJoCo and StarCraft II micro-management tasks, we show that OMIGA achieves
superior performance over the state-of-the-art offline MARL methods in almost
all tasks