5 papers
Hierarchical Molecular Representation Learning via Fragment-Based Self-Supervised Embedding Prediction
Jiele Wu, Haozhe Ma, Zhihan Guo +2
Graph self-supervised learning (GSSL) has demonstrated strong potential for generating expressive graph embeddings without the need for human annotations, making it particularly va…
MacroNav: Multi-Task Context Representation Learning Enables Efficient Navigation in Unknown Environments
Kuankuan Sima, Longbin Tang, Zhenyu Yang +2
Autonomous navigation in unknown environments requires multi-scale spatial understanding that captures geometric details, topological connectivity, and global structure to support…
Exploration by Random Reward Perturbation
Haozhe Ma, Guoji Fu, Zhengding Luo +2
We introduce Random Reward Perturbation (RRP), a novel exploration strategy for reinforcement learning (RL). Our theoretical analyses demonstrate that adding zero-mean noise to env…
Causal Policy Learning in Reinforcement Learning: Backdoor-Adjusted Soft Actor-Critic
Thanh Vinh Vo, Young Lee, Haozhe Ma +2
Hidden confounders that influence both states and actions can bias policy learning in reinforcement learning (RL), leading to suboptimal or non-generalizable behavior. Most RL algo…
Performance Asymmetry in Model-Based Reinforcement Learning
Jing Yu Lim, Rushi Shah, Zarif Ikram +4
Recently, Model-Based Reinforcement Learning (MBRL) have achieved super-human level performance on the Atari100k benchmark on average. However, we discover that conventional aggreg…