2 papers
cs.LG2025
Intrinsic Benefits of Categorical Distributional Loss: Uncertainty-aware Regularized Exploration in Reinforcement Learning
Ke Sun, Yingnan Zhao, Enze Shi +4
The remarkable empirical performance of distributional reinforcement learning (RL) has garnered increasing attention to understanding its theoretical advantages over classical RL.…
cs.LG2024
Distributional Reinforcement Learning with Regularized Wasserstein Loss
Ke Sun, Yingnan Zhao, Wulong Liu +2
The empirical success of distributional reinforcement learning (RL) highly relies on the choice of distribution divergence equipped with an appropriate distribution representation.…