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

Understand

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.