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@INPROCEEDINGS{IEEE WOWMOM 2022,
        author = {Rizzo, Gianluca},
      keywords = {Connected vehicles: networking issues and services, Internet/Web of Things, mobile and urban sensing, Participatory},
         month = jun,
         title = {Multi-Agent Data Collection in Non-Stationary Environments},
          year = {2022},
      location = {Belfast},
  organization = {IEEE WOWMOM 2022},
           url = {https://ieeexplore.ieee.org/abstract/document/9842767},
           doi = {10.1109/WoWMoM54355.2022.00023},
      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 Critically, outdated and irrelevant samples - an inherent and prevalent feature in all multi-agent MCTS-based algorithms - are filtered out by means of a sliding window mechanism.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.}
}