103 citations · 256 across the 18 of their papers we have counts for
4 papers · 1 filter
Reparameterized Policy Learning for Multimodal Trajectory Optimization
Zhiao Huang, Litian Liang, Zhan Ling +3
We investigate the challenge of parametrizing policies for reinforcement learning (RL) in high-dimensional continuous action spaces. Our objective is to develop a multimodal policy…
Improving Policy Optimization with Generalist-Specialist Learning
Zhiwei Jia, Xuanlin Li, Zhan Ling +3
Generalization in deep reinforcement learning over unseen environment variations usually requires policy learning over a large set of diverse training variations. We empirically ob…
ManiSkill: Generalizable Manipulation Skill Benchmark with Large-Scale Demonstrations
Tongzhou Mu, Zhan Ling, Fanbo Xiang +6
Object manipulation from 3D visual inputs poses many challenges on building generalizable perception and policy models. However, 3D assets in existing benchmarks mostly lack the di…
Regularization Matters in Policy Optimization
Zhuang Liu, Xuanlin Li, Bingyi Kang +1
Deep Reinforcement Learning (Deep RL) has been receiving increasingly more attention thanks to its encouraging performance on a variety of control tasks. Yet, conventional regulari…