Abstract
We consider solving a cooperative multi-robot object manipulation task using reinforcement learning (RL). We propose two distributed multi-agent RL approaches: distributed approximate RL (DA-RL), where each agent applies Q-learning with individual reward functions; and game-theoretic RL (GT-RL), where the agents update their Q-values based on the Nash equilibrium of a bimatrix Q-value game. We validate the proposed approaches in the setting of cooperative object manipulation with two simulated robot arms. Although we focus on a small system of two agents in this paper, both DA-RL and GT-RL apply to general multi-agent systems, and are expected to scale well to large systems.
| Original language | English |
|---|---|
| Title of host publication | International Conference on Autonomous Agents and Multi-Agent Systems |
| Editors | Bo An, Amal El Fallah Seghrouchni, Gita Sukthankar |
| Publisher | ACM |
| Pages | 1831-1833 |
| Number of pages | 3 |
| ISBN (Electronic) | 978-1-4503-7518-4 |
| Publication status | Published - 1 Jan 2020 |
| Event | The 19th International Conference on Autonomous Agents and Multi-Agent Systems - Auckland, New Zealand Duration: 9 May 2020 → 13 May 2020 Conference number: 19 https://aamas2020.conference.auckland.ac.nz/ https://aamas2020.conference.auckland.ac.nz |
Publication series
| Name | Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS |
|---|---|
| Volume | 2020-May |
| ISSN (Print) | 1548-8403 |
| ISSN (Electronic) | 1558-2914 |
Conference
| Conference | The 19th International Conference on Autonomous Agents and Multi-Agent Systems |
|---|---|
| Abbreviated title | AAMAS 2020 |
| Country/Territory | New Zealand |
| City | Auckland |
| Period | 9/05/20 → 13/05/20 |
| Internet address |