Name
Abstract | ||
|---|---|---|
Multiagent systems, where many agents work together to achieve their objectives, and cooperative behaviors between agents need to be realized, have been widely studied In this paper, a new reinforcement learning framework considering the concept of "Symbiosis" in order to represent complicated relationships between agents and analyze the emerging behavior is proposed. In addition, distributed state-action value tables are designed to efficiently solve the multiagent problems with large number of state-action pairs. From the simulation results, it is clarified that the proposed method shows better performance comparing to the conventional reinforcement learning without considering symbiosis. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.2991/jrnal.2015.2.1.10 | JOURNAL OF ROBOTICS NETWORKING AND ARTIFICIAL LIFE |
Keywords | Field | DocType |
reinforcement learning,symbiosis,multiagent system,cooperative behavior | Computer science,Cooperative behavior,Multi-agent system,Artificial intelligence,Reinforcement learning | Journal |
Volume | Issue | ISSN |
2 | 1 | 2352-6386 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shingo Mabu | 1 | 493 | 77.00 |
| Masanao Obayashi | 2 | 198 | 26.10 |
| Takashi Kuremoto | 3 | 196 | 27.73 |