12 citations · 19 across the 6 of their papers we have counts for
6 papers
On the Value of Myopic Behavior in Policy Reuse
Kang Xu, Chenjia Bai, Shuang Qiu +5
Leveraging learned strategies in unfamiliar scenarios is fundamental to human intelligence. In reinforcement learning, rationally reusing the policies acquired from other tasks or…
Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning
Haoran He, Chenjia Bai, Kang Xu +5
Diffusion models have demonstrated highly-expressive generative capabilities in vision and NLP. Recent studies in reinforcement learning (RL) have shown that diffusion models are a…
Cross-Domain Policy Adaptation via Value-Guided Data Filtering
Kang Xu, Chenjia Bai, Xiaoteng Ma +5
Generalizing policies across different domains with dynamics mismatch poses a significant challenge in reinforcement learning. For example, a robot learns the policy in a simulator…
Open-Ended Diverse Solution Discovery with Regulated Behavior Patterns for Cross-Domain Adaptation
Kang Xu, Yan Ma, Bingsheng Wei +1
While Reinforcement Learning can achieve impressive results for complex tasks, the learned policies are generally prone to fail in downstream tasks with even minor model mismatch o…
Quantification before Selection: Active Dynamics Preference for Robust Reinforcement Learning
Kang Xu, Yan Ma, Wei Li
Training a robust policy is critical for policy deployment in real-world systems or dealing with unknown dynamics mismatch in different dynamic systems. Domain Randomization~(DR) i…
Evolutionary Action Selection for Gradient-based Policy Learning
Yan Ma, Tianxing Liu, Bingsheng Wei +3
Evolutionary Algorithms (EAs) and Deep Reinforcement Learning (DRL) have recently been integrated to take the advantage of the both methods for better exploration and exploitation.…