20 citations · 57 across the 5 of their papers we have counts for
9 papers
Robust Imitation Learning from Noisy Demonstrations
Voot Tangkaratt, Nontawat Charoenphakdee, Masashi Sugiyama
Robust learning from noisy demonstrations is a practical but highly challenging problem in imitation learning. In this paper, we first theoretically show that robust imitation lear…
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…
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…
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt +3
Uncertainty computation in deep learning is essential to design robust and reliable systems. Variational inference (VI) is a promising approach for such computation, but requires m…