8 citations · 30 across the 20 of their papers we have counts for
11 papers · 1 filter
A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control
Zilin Kang, Chenyuan Hu, Yu Luo +3
Deep reinforcement learning for continuous control has recently achieved impressive progress. However, existing methods often suffer from primacy bias, a tendency to overfit early…
Rethinking Transformers in Solving POMDPs
Chenhao Lu, Ruizhe Shi, Yuyao Liu +3
Sequential decision-making algorithms such as reinforcement learning (RL) in real-world scenarios inevitably face environments with partial observability. This paper scrutinizes th…
ACE : Off-Policy Actor-Critic with Causality-Aware Entropy Regularization
Tianying Ji, Yongyuan Liang, Yan Zeng +7
The varying significance of distinct primitive behaviors during the policy learning process has been overlooked by prior model-free RL algorithms. Leveraging this insight, we explo…
Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss
Ruijie Zheng, Yongyuan Liang, Xiyao Wang +7
We present Premier-TACO, a multitask feature representation learning approach designed to improve few-shot policy learning efficiency in sequential decision-making tasks. Premier-T…
Generalizable Visual Reinforcement Learning with Segment Anything Model
Ziyu Wang, Yanjie Ze, Yifei Sun +2
Learning policies that can generalize to unseen environments is a fundamental challenge in visual reinforcement learning (RL). While most current methods focus on acquiring robust…
Diffusion Reward: Learning Rewards via Conditional Video Diffusion
Tao Huang, Guangqi Jiang, Yanjie Ze +1
Learning rewards from expert videos offers an affordable and effective solution to specify the intended behaviors for reinforcement learning (RL) tasks. In this work, we propose Di…
H-InDex: Visual Reinforcement Learning with Hand-Informed Representations for Dexterous Manipulation
Yanjie Ze, Yuyao Liu, Ruizhe Shi +4
Human hands possess remarkable dexterity and have long served as a source of inspiration for robotic manipulation. In this work, we propose a human andfor…
Unleashing the Power of Pre-trained Language Models for Offline Reinforcement Learning
Ruizhe Shi, Yuyao Liu, Yanjie Ze +2
Offline reinforcement learning (RL) aims to find a near-optimal policy using pre-collected datasets. In real-world scenarios, data collection could be costly and risky; therefore,…
DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization
Guowei Xu, Ruijie Zheng, Yongyuan Liang +12
Visual reinforcement learning (RL) has shown promise in continuous control tasks. Despite its progress, current algorithms are still unsatisfactory in virtually every aspect of the…
COPlanner: Plan to Roll Out Conservatively but to Explore Optimistically for Model-Based RL
Xiyao Wang, Ruijie Zheng, Yanchao Sun +4
Dyna-style model-based reinforcement learning contains two phases: model rollouts to generate sample for policy learning and real environment exploration using current policy for d…
GenSim: Generating Robotic Simulation Tasks via Large Language Models
Lirui Wang, Yiyang Ling, Zhecheng Yuan +6
Collecting large amounts of real-world interaction data to train general robotic policies is often prohibitively expensive, thus motivating the use of simulation data. However, exi…