48 citations · 99 across the 17 of their papers we have counts for
4 papers · 1 filter
Model Embedding Model-Based Reinforcement Learning
Xiaoyu Tan, Chao Qu, Junwu Xiong +1
Model-based reinforcement learning (MBRL) has shown its advantages in sample-efficiency over model-free reinforcement learning (MFRL). Despite the impressive results it achieves, i…
Neural Physicist: Learning Physical Dynamics from Image Sequences
Baocheng Zhu, Shijun Wang, James Zhang
We present a novel architecture named Neural Physicist (NeurPhy) to learn physical dynamics directly from image sequences using deep neural networks. For any physical system, given…
Riemannian Proximal Policy Optimization
Shijun Wang, Baocheng Zhu, Chen Li +4
In this paper, We propose a general Riemannian proximal optimization algorithm with guaranteed convergence to solve Markov decision process (MDP) problems. To model policy function…
Variational Policy Propagation for Multi-agent Reinforcement Learning
Chao Qu, Hui Li, Chang Liu +6
We propose a \emph{collaborative} multi-agent reinforcement learning algorithm named variational policy propagation (VPP) to learn a \emph{joint} policy through the interactions ov…