Name
Abstract | ||
|---|---|---|
In multi-robot reinforcement learning the goal is to enable a group of robots to learn coordinated behaviors from direct interaction with the environment. Here, we provide a comparison of two main approaches designed for tackling this challenge; namely, independent learners (IL) and joint-action learners (JAL). We evaluate these methods in a multi-robot cooperative and adversarial soccer scenario, called 2 versus 2 free-kick task, with simulated NAO humanoid robots as players. Our findings show that both approaches can achieve satisfying solutions, with JAL outperforming IL. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.5555/3306127.3331945 | adaptive agents and multi-agents systems |
Keywords | Field | DocType |
Multi-Robot,Deep Reinforcement Learning,Robot Soccer | Computer science,Human–computer interaction,Artificial intelligence,Robot,Machine learning,Adversarial system,Reinforcement learning,Humanoid robot | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jim Martin Catacora Ocana | 1 | 0 | 0.34 |
| Francesco Riccio | 2 | 6 | 6.28 |
| Roberto Capobianco | 3 | 40 | 9.78 |
| Daniele Nardi | 4 | 5968 | 545.67 |