32 citations · 79 across the 8 of their papers we have counts for
3 papers · 1 filter
Asking for Knowledge: Training RL Agents to Query External Knowledge Using Language
Iou-Jen Liu, Xingdi Yuan, Marc-Alexandre Côté +2
To solve difficult tasks, humans ask questions to acquire knowledge from external sources. In contrast, classical reinforcement learning agents lack such an ability and often resor…
Semantic Tracklets: An Object-Centric Representation for Visual Multi-Agent Reinforcement Learning
Iou-Jen Liu, Zhongzheng Ren, Raymond A. Yeh +1
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 l…
Cooperative Exploration for Multi-Agent Deep Reinforcement Learning
Iou-Jen Liu, Unnat Jain, Raymond A. Yeh +1
Exploration is critical for good results in deep reinforcement learning and has attracted much attention. However, existing multi-agent deep reinforcement learning algorithms still…