Name
Title | ||
|---|---|---|
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks. |
Abstract | ||
|---|---|---|
Multi-agent deep reinforcement learning (MARL) suffers from a lack of commonly-used evaluation tasks and criteria, making comparisons between approaches difficult. In this work, we provide a systematic evaluation and comparison of three different classes of MARL algorithms (independent learning, centralised multi-agent policy gradient, value decomposition) in a diverse range of cooperative multi-agent learning tasks. Our experiments serve as a reference for the expected performance of algorithms across different learning tasks, and we provide insights regarding the effectiveness of different learning approaches. We open-source EPyMARL, which extends the PyMARL codebase to include additional algorithms and allow for flexible configuration of algorithm implementation details such as parameter sharing. Finally, we open-source two environments for multi-agent research which focus on coordination under sparse rewards. |
Year | Venue | DocType |
|---|---|---|
2021 | Annual Conference on Neural Information Processing Systems | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Georgios Papoudakis | 1 | 0 | 0.68 |
| Filippos Christianos | 2 | 0 | 3.38 |
| Lukas Schäfer | 3 | 0 | 1.35 |
| Stefano V. Albrecht | 4 | 0 | 0.34 |