3 papers
cs.LG2026
Compositional Behavioral Semantics for State Abstraction in Reinforcement Learning
Yivan Zhang, Ziyan Luo, Manuel Baltieri
State abstraction plays a key role in scaling reinforcement learning to complex but structured systems. In studying such systems, a wide range of behavioral structures have been st…
cs.LG2025
Recursive Reward Aggregation
Yuting Tang, Yivan Zhang, Johannes Ackermann +3
In reinforcement learning (RL), aligning agent behavior with specific objectives typically requires careful design of the reward function, which can be challenging when the desired…
cs.LG2024
Enriching Disentanglement: From Logical Definitions to Quantitative Metrics
Yivan Zhang, Masashi Sugiyama
Disentangling the explanatory factors in complex data is a promising approach for generalizable and data-efficient representation learning. While a variety of quantitative metrics…