Learning to Coordinate without Communication under Incomplete Information
<|reference_start|>Learning to Coordinate without Communication under Incomplete Information: Achieving seamless coordination in cooperative games is a crucial challenge in artificial intelligence, particularly when players operate under incomplete information. A common strategy to mitigate this information asymmetry involves leveraging explicit communication. However, direct communication is not always feasible due to factors such as transmission loss. We explore how effective coordination can be achieved without verbal communication, relying solely on observing each other's actions. We demonstrate how an autonomous agent can learn to cooperate by interpreting its partner's actions, which are used to hint at its intents. Our approach involves developing an agent strategy by constructing deterministic finite automata for each possible action and integrating them into a non-Markovian finite-state transducer. This transducer represents a non-deterministic strategy for the agent that suggests actions to assist its partner during gameplay. Experimental results in a testbed called Gnomes at Night show that the learned no-communication coordination strategy achieves significantly higher success rates and requires fewer steps to complete the game compared to uncoordinated scenarios, performing almost as well as an oracle baseline with direct communication.<|reference_end|>
@article{chen2024learning,
    title={Learning to Coordinate without Communication under Incomplete
  Information},
    author={Shenghui Chen, Shufang Zhu, Giuseppe De Giacomo, Ufuk Topcu},
    journal={arXiv preprint arXiv:2409.12397},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.12397},
    primaryClass={cs.AI}
}