Voot Tangkaratt | DeepAI

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∙ 03/12/2021

Discovering Diverse Solutions in Deep Reinforcement Learning

Reinforcement learning (RL) algorithms are typically limited to learning...

0 Takayuki Osa, et al. ∙

∙ 10/20/2020

Robust Imitation Learning from Noisy Demonstrations

Learning from noisy demonstrations is a practical but highly challenging...

0 Voot Tangkaratt, et al. ∙

∙ 06/04/2020

Meta-Model-Based Meta-Policy Optimization

Model-based reinforcement learning (MBRL) has been applied to meta-learn...

0 Takuya Hiraoka, et al. ∙

∙ 09/15/2019

VILD: Variational Imitation Learning with Diverse-quality Demonstrations

The goal of imitation learning (IL) is to learn a good policy from high-...

0 Voot Tangkaratt, et al. ∙

∙ 01/27/2019

Imitation Learning from Imperfect Demonstration

Imitation learning (IL) aims to learn an optimal policy from demonstrati...

14 Yueh-Hua Wu, et al. ∙

∙ 01/05/2019

Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization

Real-world tasks are often highly structured. Hierarchical reinforcement...

0 Takayuki Osa, et al. ∙

∙ 12/19/2018

TD-Regularized Actor-Critic Methods

Actor-critic methods can achieve incredible performance on difficult rei...

4 Simone Parisi, et al. ∙

∙ 12/06/2018

Active Deep Q-learning with Demonstration

Recent research has shown that although Reinforcement Learning (RL) can ...

4 Si-An Chen, et al. ∙

∙ 06/13/2018

Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam

Uncertainty computation in deep learning is essential to design robust a...

0 Mohammad Emtiyaz Khan, et al. ∙

∙ 12/04/2017

Vprop: Variational Inference using RMSprop

Many computationally-efficient methods for Bayesian deep learning rely o...

0 Mohammad Emtiyaz Khan, et al. ∙

∙ 11/15/2017

Variational Adaptive-Newton Method for Explorative Learning

We present the Variational Adaptive Newton (VAN) method which is a black...

0 Mohammad Emtiyaz Khan, et al. ∙

∙ 05/22/2017

Deep Reinforcement Learning with Relative Entropy Stochastic Search

Many reinforcement learning methods for continuous control tasks are bas...

0 Voot Tangkaratt, et al. ∙

∙ 11/10/2016

Policy Search with High-Dimensional Context Variables

Direct contextual policy search methods learn to improve policy paramete...

0 Voot Tangkaratt, et al. ∙

∙ 08/05/2015

Direct Estimation of the Derivative of Quadratic Mutual Information with Application in Supervised Dimension Reduction

A typical goal of supervised dimension reduction is to find a low-dimens...

0 Voot Tangkaratt, et al. ∙

∙ 04/28/2014

Conditional Density Estimation with Dimensionality Reduction via Squared-Loss Conditional Entropy Minimization

Regression aims at estimating the conditional mean of output given input...

0 Voot Tangkaratt, et al. ∙

∙ 07/19/2013

Model-Based Policy Gradients with Parameter-Based Exploration by Least-Squares Conditional Density Estimation

The goal of reinforcement learning (RL) is to let an agent learn an opti...

0 Syogo Mori, et al. ∙

∙ 01/17/2013

Efficient Sample Reuse in Policy Gradients with Parameter-based Exploration

The policy gradient approach is a flexible and powerful reinforcement le...

0 Tingting Zhao, et al. ∙

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