Name
Title | ||
|---|---|---|
Semantic Tracklets: An Object-Centric Representation for Visual Multi-Agent Reinforcement Learning |
Abstract | ||
|---|---|---|
Solving complex real-world tasks, e.g., autonomous fleet control, often involves a coordinated team of multiple agents which learn strategies from visual inputs via reinforcement learning. Many existing multi-agent reinforcement learning (MARL) algorithms however don't scale to environments where agents operate on visual inputs. To address this issue, algorithmically, recent works have focused on non-stationarity and exploration. In contrast, we study whether scalability can also be achieved via a disentangled representation. For this, we explicitly construct an object-centric intermediate representation to characterize the states of an environment, which we refer to as 'semantic tracklets.' We evaluate 'semantic tracklets' on the visual multi-agent particle environment (VMPE) and on the challenging visual multi-agent GFootball environment. 'Semantic tracklets' consistently outperform baselines on VMPE, and achieve a +2.4 higher score difference than baselines on GFootball. Notably, this method is the first to successfully learn a strategy for five players in the GFootball environment using only visual data. For more, please see our project page: https://ioujenliu.github.io/SemanticTracklets |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IROS51168.2021.9636592 | 2021 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) |
DocType | ISSN | Citations |
Conference | 2153-0858 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Iou-Jen Liu | 1 | 12 | 2.64 |
| Zhongzheng Ren | 2 | 0 | 0.34 |
| Raymond Yeh | 3 | 67 | 6.83 |
| Alexander G. Schwing | 4 | 696 | 51.78 |