151 citations · 542 across the 33 of their papers we have counts for
6 papers · 1 filter
Learning Long-Term Reward Redistribution via Randomized Return Decomposition
Zhizhou Ren, Ruihan Guo, Yuan Zhou +1
Many practical applications of reinforcement learning require agents to learn from sparse and delayed rewards. It challenges the ability of agents to attribute their actions to fut…
Hindsight Foresight Relabeling for Meta-Reinforcement Learning
Michael Wan, Jian Peng, Tanmay Gangwani
Meta-reinforcement learning (meta-RL) algorithms allow for agents to learn new behaviors from small amounts of experience, mitigating the sample inefficiency problem in RL. However…
Coordinate-wise Control Variates for Deep Policy Gradients
Yuanyi Zhong, Yuan Zhou, Jian Peng
The control variates (CV) method is widely used in policy gradient estimation to reduce the variance of the gradient estimators in practice. A control variate is applied by subtrac…
Off-Policy Reinforcement Learning with Delayed Rewards
Beining Han, Zhizhou Ren, Zuofan Wu +2
We study deep reinforcement learning (RL) algorithms with delayed rewards. In many real-world tasks, instant rewards are often not readily accessible or even defined immediately af…
Learning Neural Generative Dynamics for Molecular Conformation Generation
Minkai Xu, Shitong Luo, Yoshua Bengio +2
We study how to generate molecule conformations (i.e., 3D structures) from a molecular graph. Traditional methods, such as molecular dynamics, sample conformations via computationa…
DAP: Detection-Aware Pre-training with Weak Supervision
Yuanyi Zhong, Jianfeng Wang, Lijuan Wang +3
This paper presents a detection-aware pre-training (DAP) approach, which leverages only weakly-labeled classification-style datasets (e.g., ImageNet) for pre-training, but is speci…