Multi-Agent Data Collection in Non-Stationary Environments
| Type of publication: | Inproceedings |
| Citation: | IEEE WOWMOM 2022 |
| Publication status: | Published |
| Year: | 2022 |
| Month: | June |
| Location: | Belfast |
| Organization: | IEEE WOWMOM 2022 |
| URL: | https://ieeexplore.ieee.org/ab... |
| 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 Critically, outdated and irrelevant samples - an inherent and prevalent feature in all multi-agent MCTS-based algorithms - are filtered out by means of a sliding window mechanism.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. |
| Keywords: | Connected vehicles: networking issues and services, Internet/Web of Things, mobile and urban sensing, Participatory |
| Authors | |
| Added by: | [] |
| Total mark: | 0 |
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