32 citations · 74 across the 6 of their papers we have counts for
4 papers · 1 filter
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…