@inproceedings{discovery10193055,
           title = {Online Multi-Robot Coverage Path Planning in Dynamic Environments Through Pheromone-Based Reinforcement Learning},
            year = {2024},
       publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
         journal = {2024 IEEE 20th International Conference on Automation Science and Engineering},
           month = {October},
          series = {IEEE International Conference on Automation Science and Engineering (CASE)},
       booktitle = {Proceedings of the 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE)},
           pages = {1000--1005},
          volume = {20},
            note = {This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.},
         address = {Bari, Italy},
             url = {https://doi.org/10.1109/CASE59546.2024.10711550},
        abstract = {Two promising approaches to coverage path planning are reward-based and pheromone-based methods. Rewardbased methods allow heuristics to be learned automatically, often yielding a superior performance over hand-crafted rules. On the other hand, pheromone-based methods consistently demonstrate superior generalization and adaptation abilities when placed in unfamiliar environments. To obtain the best of both worlds, we introduce Greedy Entropy Maximization (GEM), a hybrid approach that aims to maximize the entropy of a pheromone deposited by a swarm of homogeneous antlike agents. We begin by establishing a sharp upper-bound on achievable entropy and show that this corresponds to optimal dynamic coverage path planning. Next, we demonstrate that GEM closely approaches this upper-bound despite depriving agents of basic necessities such as memory and explicit communication. Finally, we show that GEM can be executed asynchronously in constant-time, enabling it to scale arbitrarily.},
            issn = {2161-8089},
          author = {Champagnie, Kale and Chen, Boli and Arvin, Farshad and Hu, Junyan},
        keywords = {Computer aided software engineering, Automation, Neural networks, Reinforcement learning, Path planning, Entropy}
}