11 citations · 32 across the 13 of their papers we have counts for
6 papers · 1 filter
Training Discrete Deep Generative Models via Gapped Straight-Through Estimator
Ting-Han Fan, Ta-Chung Chi, Alexander I. Rudnicky +1
While deep generative models have succeeded in image processing, natural language processing, and reinforcement learning, training that involves discrete random variables remains c…
Explaining Off-Policy Actor-Critic From A Bias-Variance Perspective
Ting-Han Fan, Peter J. Ramadge
Off-policy Actor-Critic algorithms have demonstrated phenomenal experimental performance but still require better explanations. To this end, we show its policy evaluation error on…
Soft Actor-Critic With Integer Actions
Ting-Han Fan, Yubo Wang
Reinforcement learning is well-studied under discrete actions. Integer actions setting is popular in the industry yet still challenging due to its high dimensionality. To this end,…
PowerGym: A Reinforcement Learning Environment for Volt-Var Control in Power Distribution Systems
Ting-Han Fan, Xian Yeow Lee, Yubo Wang
We introduce PowerGym, an open-source reinforcement learning environment for Volt-Var control in power distribution systems. Following OpenAI Gym APIs, PowerGym targets minimizing…
A Contraction Approach to Model-based Reinforcement Learning
Ting-Han Fan, Peter J. Ramadge
Despite its experimental success, Model-based Reinforcement Learning still lacks a complete theoretical understanding. To this end, we analyze the error in the cumulative reward us…
Model Imitation for Model-Based Reinforcement Learning
Yueh-Hua Wu, Ting-Han Fan, Peter J. Ramadge +1
Model-based reinforcement learning (MBRL) aims to learn a dynamic model to reduce the number of interactions with real-world environments. However, due to estimation error, rollout…