4 papers · 1 filter
Transferable Delay-Aware Reinforcement Learning via Implicit Causal Graph Modeling
Chenran Zhao, Dianxi Shi, Yaowen Zhang +2
Random delays weaken the temporal correspondence between actions and subsequent state feedback, making it difficult for agents to identify the true propagation process of action ef…
Delay-Empowered Causal Hierarchical Reinforcement Learning
Chenran Zhao, Dianxi Shi, Haotian Wang +4
Many real-world tasks involve delayed effects, where the outcomes of actions emerge after varying time lags. Existing delay-aware reinforcement learning methods often rely on state…
Separation and Collaboration: Two-Level Routing Grouped Mixture-of-Experts for Multi-Domain Continual Learning
Jialu Zhou, Dianxi Shi, Shaowu Yang +5
Multi-Domain Continual Learning (MDCL) acquires knowledge from sequential tasks with shifting class sets and distribution. Despite the Parameter-Efficient Fine-Tuning (PEFT) method…
D3HRL: A Distributed Hierarchical Reinforcement Learning Approach Based on Causal Discovery and Spurious Correlation Detection
Chenran Zhao, Dianxi Shi, Mengzhu Wang +5
Current Hierarchical Reinforcement Learning (HRL) algorithms excel in long-horizon sequential decision-making tasks but still face two challenges: delay effects and spurious correl…