Semantic Tracklets: An Object-Centric Representation for Visual Multi-Agent Reinforcement Learning

• URL: http://arxiv.org/abs/2108.03319v1
• Date: Fri, 6 Aug 2021 22:19:09 GMT
• Title: Semantic Tracklets: An Object-Centric Representation for Visual Multi-Agent Reinforcement Learning
• Authors: Iou-Jen Liu, Zhongzheng Ren, Raymond A. Yeh, Alexander G. Schwing
• Abstract summary: We study whether scalability can be achieved via a disentangled representation. We evaluate semantic tracklets' on the visual multi-agent particle environment (VMPE) and on the challenging visual multi-agent GFootball environment. Notably, this method is the first to successfully learn a strategy for five players in the GFootball environment using only visual data.
• Score: 126.57680291438128
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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