Name
Title | ||
|---|---|---|
The Impact of Agent Definitions and Interactions on Multiagent Learning for Coordination |
Abstract | ||
|---|---|---|
The state-action space of an individual agent in a multiagent team fundamentally dictates how the individual interacts with the rest of the team. Thus, how an agent is defined in the context of its domain has a significant effect on team performance when learning to coordinate. In this work we explore the trade-offs associated with these design choices, for example, having fewer agents in the team that individually are able to process and act on a wider scope of information about the world versus a larger team of agents where each agent observes and acts in a more local region of the domain. We focus our study on a traffic management domain and highlight the trends in learning performance when applying different agent definitions. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.5555/3306127.3331908 | adaptive agents and multi-agents systems |
Keywords | Field | DocType |
AAMAS,ACM proceedings | Computer science,Human–computer interaction,Multiagent learning,Distributed computing | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jen Jen Chung | 1 | 21 | 9.92 |
| Damjan Miklic | 2 | 36 | 6.92 |
| Lorenzo Sabattini | 3 | 393 | 36.65 |
| kagan tumer | 4 | 1632 | 168.61 |
| Roland Siegwart | 5 | 7640 | 551.49 |