59 citations · 194 across the 18 of their papers we have counts for
18 papers
Discovering Diverse Solutions in Deep Reinforcement Learning by Maximizing State-Action-Based Mutual Information
Takayuki Osa, Voot Tangkaratt, Masashi Sugiyama
Reinforcement learning algorithms are typically limited to learning a single solution for a specified task, even though diverse solutions often exist. Recent studies showed that le…
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…
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…
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…
Vprop: Variational Inference using RMSprop
Mohammad Emtiyaz Khan, Zuozhu Liu, Voot Tangkaratt +1
Many computationally-efficient methods for Bayesian deep learning rely on continuous optimization algorithms, but the implementation of these methods requires significant changes t…
Variational Adaptive-Newton Method for Explorative Learning
Mohammad Emtiyaz Khan, Wu Lin, Voot Tangkaratt +2
We present the Variational Adaptive Newton (VAN) method which is a black-box optimization method especially suitable for explorative-learning tasks such as active learning and rein…
Guide Actor-Critic for Continuous Control
Voot Tangkaratt, Abbas Abdolmaleki, Masashi Sugiyama
Actor-critic methods solve reinforcement learning problems by updating a parameterized policy known as an actor in a direction that increases an estimate of the expected return kno…
Policy Search with High-Dimensional Context Variables
Voot Tangkaratt, Herke van Hoof, Simone Parisi +3
Direct contextual policy search methods learn to improve policy parameters and simultaneously generalize these parameters to different context or task variables. However, learning…
Direct Estimation of the Derivative of Quadratic Mutual Information with Application in Supervised Dimension Reduction
Voot Tangkaratt, Hiroaki Sasaki, Masashi Sugiyama
A typical goal of supervised dimension reduction is to find a low-dimensional subspace of the input space such that the projected input variables preserve maximal information about…
Efficient Reuse of Previous Experiences to Improve Policies in Real Environment
Norikazu Sugimoto, Voot Tangkaratt, Thijs Wensveen +3
In this study, we show that a movement policy can be improved efficiently using the previous experiences of a real robot. Reinforcement Learning (RL) is becoming a popular approach…
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…
Model-Based Policy Gradients with Parameter-Based Exploration by Least-Squares Conditional Density Estimation
Syogo Mori, Voot Tangkaratt, Tingting Zhao +2
The goal of reinforcement learning (RL) is to let an agent learn an optimal control policy in an unknown environment so that future expected rewards are maximized. The model-free R…
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…