4 papers · 1 filter
Generalized Munchausen Reinforcement Learning using Tsallis KL Divergence
Lingwei Zhu, Zheng Chen, Matthew Schlegel +1
Many policy optimization approaches in reinforcement learning incorporate a Kullback-Leilbler (KL) divergence to the previous policy, to prevent the policy from changing too quickl…
Automated Cancer Subtyping via Vector Quantization Mutual Information Maximization
Zheng Chen, Lingwei Zhu, Ziwei Yang +1
Cancer subtyping is crucial for understanding the nature of tumors and providing suitable therapy. However, existing labelling methods are medically controversial, and have driven…
Enforcing KL Regularization in General Tsallis Entropy Reinforcement Learning via Advantage Learning
Lingwei Zhu, Zheng Chen, Eiji Uchibe +1
Maximum Tsallis entropy (MTE) framework in reinforcement learning has gained popularity recently by virtue of its flexible modeling choices including the widely used Shannon entrop…
-Munchausen Reinforcement Learning
Lingwei Zhu, Zheng Chen, Eiji Uchibe +1
The recently successful Munchausen Reinforcement Learning (M-RL) features implicit Kullback-Leibler (KL) regularization by augmenting the reward function with logarithm of the curr…