59 citations · 194 across the 18 of their papers we have counts for
8 papers · 1 filter
Meta-Model-Based Meta-Policy Optimization
Takuya Hiraoka, Takahisa Imagawa, Voot Tangkaratt +3
Model-based meta-reinforcement learning (RL) methods have recently been shown to be a promising approach to improving the sample efficiency of RL in multi-task settings. However, t…
VILD: Variational Imitation Learning with Diverse-quality Demonstrations
Voot Tangkaratt, Bo Han, Mohammad Emtiyaz Khan +1
The goal of imitation learning (IL) is to learn a good policy from high-quality demonstrations. However, the quality of demonstrations in reality can be diverse, since it is easier…
Imitation Learning from Imperfect Demonstration
Yueh-Hua Wu, Nontawat Charoenphakdee, Han Bao +2
Imitation learning (IL) aims to learn an optimal policy from demonstrations. However, such demonstrations are often imperfect since collecting optimal ones is costly. To effectivel…
Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization
Takayuki Osa, Voot Tangkaratt, Masashi Sugiyama
Real-world tasks are often highly structured. Hierarchical reinforcement learning (HRL) has attracted research interest as an approach for leveraging the hierarchical structure of…
Active Deep Q-learning with Demonstration
Si-An Chen, Voot Tangkaratt, Hsuan-Tien Lin +1
Recent research has shown that although Reinforcement Learning (RL) can benefit from expert demonstration, it usually takes considerable efforts to obtain enough demonstration. The…
TD-Regularized Actor-Critic Methods
Simone Parisi, Voot Tangkaratt, Jan Peters +1
Actor-critic methods can achieve incredible performance on difficult reinforcement learning problems, but they are also prone to instability. This is partly due to the interaction…
Conditional Density Estimation with Dimensionality Reduction via Squared-Loss Conditional Entropy Minimization
Voot Tangkaratt, Ning Xie, Masashi Sugiyama
Regression aims at estimating the conditional mean of output given input. However, regression is not informative enough if the conditional density is multimodal, heteroscedastic, a…
Efficient Sample Reuse in Policy Gradients with Parameter-based Exploration
Tingting Zhao, Hirotaka Hachiya, Voot Tangkaratt +2
The policy gradient approach is a flexible and powerful reinforcement learning method particularly for problems with continuous actions such as robot control. A common challenge in…