Chatzinotas, Symeon[University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Ottersten, Björn[University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
11-Sep-2019
PIMRC 2019 Proceedings
Mostaani, Arsham
Simeone, Osvaldo
Chatzinotas, Symeon
Ottersten, Björn
IEEE
Yes
No
International
Istanbul
Turkey
IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)
08-09-2019 to 11-09-2019
IEEE
Istanbul
Turkey
[en] Reinforcement learning ; Communication Theory ; Multi-agent systems
[en] Consider a collaborative task carried out by two autonomous agents that can communicate over a noisy channel. Each agent is only aware of its own state, while the accomplishment of the task depends on the value of the joint state of both agents. As an example, both agents must simultaneously reach a certain location of the environment, while only being aware of their own positions. Assuming the presence of feedback in the form of a common reward to the agents, a conventional approach would apply separately: (\emph{i}) an off-the-shelf coding and decoding scheme in order to enhance the reliability of the communication of the state of one agent to the other; and (\emph{ii}) a standard multiagent reinforcement learning strategy to learn how to act in the resulting environment. In this work, it is argued that the performance of the collaborative task can be improved if the agents learn how to jointly communicate and act. In particular, numerical results for a baseline grid world example demonstrate that the jointly learned policy carries out compression and unequal error protection by leveraging information about the action policy.
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Applied Security and Information Assurance Group (APSIA)