163 citations · 242 across the 8 of their papers we have counts for
8 papers
Agent-Centric Representations for Multi-Agent Reinforcement Learning
Wenling Shang, Lasse Espeholt, Anton Raichuk +1
Object-centric representations have recently enabled significant progress in tackling relational reasoning tasks. By building a strong object-centric inductive bias into neural arc…
GEM: Group Enhanced Model for Learning Dynamical Control Systems
Philippe Hansen-Estruch, Wenling Shang, Lerrel Pinto +2
Learning the dynamics of a physical system wherein an autonomous agent operates is an important task. Often these systems present apparent geometric structures. For instance, the t…
Reinforcement Learning with Latent Flow
Wenling Shang, Xiaofei Wang, Aravind Srinivas +4
Temporal information is essential to learning effective policies with Reinforcement Learning (RL). However, current state-of-the-art RL algorithms either assume that such informati…
Learning World Graphs to Accelerate Hierarchical Reinforcement Learning
Wenling Shang, Alex Trott, Stephan Zheng +2
In many real-world scenarios, an autonomous agent often encounters various tasks within a single complex environment. We propose to build a graph abstraction over the environment s…
ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games
Yuandong Tian, Qucheng Gong, Wenling Shang +2
In this paper, we propose ELF, an Extensive, Lightweight and Flexible platform for fundamental reinforcement learning research. Using ELF, we implement a highly customizable real-t…
Channel-Recurrent Autoencoding for Image Modeling
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 t…