no code implementations • 19 Apr 2021 • Wenling Shang, Lasse Espeholt, Anton Raichuk, Tim Salimans
Empirically, agent-centric representation learning leads to the emergence of more complex cooperation strategies between agents as well as enhanced sample efficiency and generalization.
no code implementations • 7 Apr 2021 • Philippe Hansen-Estruch, Wenling Shang, Lerrel Pinto, Pieter Abbeel, Stas Tiomkin
In this work, we take advantage of these structures to build effective dynamical models that are amenable to sample-based learning.
2 code implementations • NeurIPS 2021 • Wenling Shang, Xiaofei Wang, Aravind Srinivas, Aravind Rajeswaran, Yang Gao, Pieter Abbeel, Michael Laskin
Temporal information is essential to learning effective policies with Reinforcement Learning (RL).
no code implementations • 25 Sep 2019 • Wenling Shang, Alex Trott, Stephan Zheng, Caiming Xiong, Richard Socher
Efficiently learning to solve tasks in complex environments is a key challenge for reinforcement learning (RL) agents.
no code implementations • 1 Jul 2019 • Wenling Shang, Alex Trott, Stephan Zheng, Caiming Xiong, Richard Socher
We perform a thorough ablation study to evaluate our approach on a suite of challenging maze tasks, demonstrating significant advantages from the proposed framework over baselines that lack world graph knowledge in terms of performance and efficiency.
Hierarchical Reinforcement Learning reinforcement-learning +1
no code implementations • ICLR 2019 • Kihyuk Sohn, Wenling Shang, Xiang Yu, Manmohan Chandraker
Unsupervised domain adaptation is a promising avenue to enhance the performance of deep neural networks on a target domain, using labels only from a source domain.
2 code implementations • NeurIPS 2017 • Yuandong Tian, Qucheng Gong, Wenling Shang, Yuxin Wu, C. Lawrence Zitnick
In addition, our platform is flexible in terms of environment-agent communication topologies, choices of RL methods, changes in game parameters, and can host existing C/C++-based game environments like Arcade Learning Environment.
no code implementations • 12 Jun 2017 • Wenling Shang, Kihyuk Sohn, Yuandong Tian
Despite recent successes in synthesizing faces and bedrooms, existing generative models struggle to capture more complex image types, potentially due to the oversimplification of their latent space constructions.
2 code implementations • 16 Mar 2016 • Wenling Shang, Kihyuk Sohn, Diogo Almeida, Honglak Lee
Recently, convolutional neural networks (CNNs) have been used as a powerful tool to solve many problems of machine learning and computer vision.
no code implementations • NeurIPS 2014 • Kihyuk Sohn, Wenling Shang, Honglak Lee
Deep learning has been successfully applied to multimodal representation learning problems, with a common strategy to learning joint representations that are shared across multiple modalities on top of layers of modality-specific networks.