32 citations · 79 across the 8 of their papers we have counts for
8 papers
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…
GridToPix: Training Embodied Agents with Minimal Supervision
Unnat Jain, Iou-Jen Liu, Svetlana Lazebnik +3
While deep reinforcement learning (RL) promises freedom from hand-labeled data, great successes, especially for Embodied AI, require significant work to create supervision via care…
High-Throughput Synchronous Deep RL
Iou-Jen Liu, Raymond A. Yeh, Alexander G. Schwing
Deep reinforcement learning (RL) is computationally demanding and requires processing of many data points. Synchronous methods enjoy training stability while having lower data thro…
Bridging the Imitation Gap by Adaptive Insubordination
Luca Weihs, Unnat Jain, Iou-Jen Liu +4
In practice, imitation learning is preferred over pure reinforcement learning whenever it is possible to design a teaching agent to provide expert supervision. However, we show tha…
PIC: Permutation Invariant Critic for Multi-Agent Deep Reinforcement Learning
Iou-Jen Liu, Raymond A. Yeh, Alexander G. Schwing
Sample efficiency and scalability to a large number of agents are two important goals for multi-agent reinforcement learning systems. Recent works got us closer to those goals, add…
Knowledge Flow: Improve Upon Your Teachers
Iou-Jen Liu, Jian Peng, Alexander G. Schwing
A zoo of deep nets is available these days for almost any given task, and it is increasingly unclear which net to start with when addressing a new task, or which net to use as an i…