Multi-Agent Data Collection in Non-Stationary Environments
Type of publication: | Inproceedings |
Citation: | IEEE WOWMOM 2022 |
Year: | 2022 |
Month: | June |
Location: | Belfast |
Organization: | IEEE WOWMOM 2022 |
Abstract: | Coordinated multi-robot systems are an effective way to harvest data from sensor network and to implement active perception strategies. However, achieving efficient coordination in a way which guarantees a target QoS while adapting dynamically to changes (in the environment, due to sensors' mobility, and/or in the value of harvested data) is to date a key open issue. In this paper, we propose a novel decentralized Monte Carlo Tree Search algorithm (MCTS) which allows agents to optimize their own actions while achieving some form of coordination, in a changing environment. Its key underlying idea is to balance in an adaptive manner the exploration-exploitation trade-off to deal effectively with abrupt changes caused by the environment and random changes caused by other agents' actions. Critically, outdated and irrelevant samples - an inherent and prevalent feature in all multi-agent MCTS-based algorithms - are filtered out by means of a sliding window mechanism. We show both theoretically and through simulations that our algorithm provides a log-factor (in terms of time steps) smaller regret than state-of-the-art decentralized multi-agent planning methods. We instantiate our approach on the problem of underwater data collection, showing on a set of different models for changes that our approach greatly outperforms the best available algorithms for that setting, both in terms of convergence speed and of global utility. |
Keywords: | Connected vehicles: networking issues and services, Internet/Web of Things, mobile and urban sensing, Participatory |
Authors | |
Added by: | [] |
Total mark: | 0 |
Attachments
|
|
Notes
|
|
|
|
Topics
|
|
|