Many real-world reinforcement learning tasks require multiple agents to make sequential decisions under the agents' interaction, where well-coordinated actions among the agents are crucial to achieve the target goal better at these tasks. One way to accelerate the coordination effect is to enable multiple agents to communicate with each other in a distributed manner and behave as a group. In this paper, we study a practical scenario when (i) the communication bandwidth is limited and (ii) the agents share the communication medium so that only a restricted number of agents are able to simultaneously use the medium, as in the state-of-the-art wireless networking standards. This calls for a certain form of communication scheduling. In that regard, we propose a multi-agent deep reinforcement learning framework, called SchedNet, in which agents learn how to schedule themselves, how to encode the messages, and how to select actions based on received messages. SchedNet is capable of deciding which agents should be entitled to broadcasting their (encoded) messages, by learning the importance of each agent's partially observed information. We evaluate SchedNet against multiple baselines under two different applications, namely, cooperative communication and navigation, and predator-prey. Our experiments show a non-negligible performance gap between SchedNet and other mechanisms such as the ones without communication and with vanilla scheduling methods, e.g., round robin, ranging from 32% to 43%.
|Publication status||Published - 2019|
|Event||7th International Conference on Learning Representations, ICLR 2019 - New Orleans, United States|
Duration: 2019 May 6 → 2019 May 9
|Conference||7th International Conference on Learning Representations, ICLR 2019|
|Period||19/5/6 → 19/5/9|
Bibliographical noteFunding Information:
This work was supported by Institute for Information communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (No.2018-0-00170, Virtual Presence in Moving Objects through 5G)
© 7th International Conference on Learning Representations, ICLR 2019. All Rights Reserved.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Linguistics and Language
- Language and Linguistics