Principled Learning-to-Communicate with Quasi-Classical Information Structures
Learning-to-communicate (LTC) in partially observable environments has received increasing attention in deep multi-agent reinforcement learning, where the control and communication strategies are jointly learned. Meanwhile, the impact of communication on decision-making has been extensively studied in control theory. In this paper, we seek to formalize and better understand LTC by bridging these two lines of work, through the lens of information structures (ISs). To this end, we formalize LTC in decentralized partially observable Markov decision processes (Dec-POMDPs) under the common-information-based framework from decentralized stochastic control, and classify LTC problems based on the...
| Authors | Xiangyu Liu, Haoyi You, Kaiqing Zhang |
|---|---|
| Primary category | eess.SY |
| Published | 4 March 2026 |
| Last updated at source | 29 April 2026 |
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