4 citations · 17 across the 15 of their papers we have counts for
4 papers · 1 filter
Beyond Exact Gradients: Convergence of Stochastic Soft-Max Policy Gradient Methods with Entropy Regularization
Yuhao Ding, Junzi Zhang, Hyunin Lee +1
Entropy regularization is an efficient technique for encouraging exploration and preventing a premature convergence of (vanilla) policy gradient methods in reinforcement learning (…
On the Global Optimum Convergence of Momentum-based Policy Gradient
Yuhao Ding, Junzi Zhang, Javad Lavaei
Policy gradient (PG) methods are popular and efficient for large-scale reinforcement learning due to their relative stability and incremental nature. In recent years, the empirical…
A Dual Approach to Constrained Markov Decision Processes with Entropy Regularization
Donghao Ying, Yuhao Ding, Javad Lavaei
We study entropy-regularized constrained Markov decision processes (CMDPs) under the soft-max parameterization, in which an agent aims to maximize the entropy-regularized value fun…
Ontology-Enhanced Slot Filling
Yuhao Ding, Yik-Cheung Tam
Slot filling is a fundamental task in dialog state tracking in task-oriented dialog systems. In multi-domain task-oriented dialog system, user utterances and system responses may m…