TY  - CONF
ID  - IEEE WOWMOM 2022
T1  - Multi-Agent Data Collection in Non-Stationary Environments
A1  - Rizzo, Gianluca
Y1  - 2022
T2  - IEEE WOWMOM 2022
CY  - Belfast
UR  - https://ieeexplore.ieee.org/abstract/document/9842767
M2  - doi: 10.1109/WoWMoM54355.2022.00023
KW  - Connected vehicles: networking issues and services
KW  - Internet/Web of Things
KW  - mobile and urban sensing
KW  - Participatory
N2  - 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 Critically, outdated and irrelevant samples - an inherent and prevalent feature in all multi-agent MCTS-based algorithms - are filtered out by means of a sliding window mechanism.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.
ER  -