Learning to Seek: Multi-Agent Online Source Seeking Against Non-Stochastic Disturbances
This work addresses cooperative source seeking for applications like pollution monitoring, but it is incremental as it builds on existing techniques with standard assumptions.
The paper tackles the problem of multi-agent online source seeking in unknown, dynamically changing environments with non-stochastic disturbances, achieving sub-linear regrets comparable to state-of-the-art results.
This paper proposes to leverage the emerging~learning techniques and devise a multi-agent online source {seeking} algorithm under unknown environment. Of particular significance in our problem setups are: i) the underlying environment is not only unknown, but dynamically changing and also perturbed by two types of non-stochastic disturbances; and ii) a group of agents is deployed and expected to cooperatively seek as many sources as possible. Correspondingly, a new technique of discounted Kalman filter is developed to tackle with the non-stochastic disturbances, and a notion of confidence bound in polytope nature is utilized~to aid the computation-efficient cooperation among~multiple agents. With standard assumptions on the unknown environment as well as the disturbances, our algorithm is shown to achieve sub-linear regrets under the two~types of non-stochastic disturbances; both results are comparable to the state-of-the-art. Numerical examples on a real-world pollution monitoring application are provided to demonstrate the effectiveness of our algorithm.