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Found 18 papers, 7 papers with code
Variational Imitation Learning with Diverse-quality Demonstrations
Learning from demonstrations can be challenging when the quality of demonstrations is diverse, and even more so when the quality is unknown and there is no additional information to estimate the quality.
Discovering Diverse Solutions in Deep Reinforcement Learning by Maximizing State-Action-Based Mutual Information
In our method, a policy conditioned on a continuous or discrete latent variable is trained by directly maximizing the variational lower bound of the mutual information, instead of using the mutual information as unsupervised rewards as in previous studies.
Robust Imitation Learning from Noisy Demonstrations
Robust learning from noisy demonstrations is a practical but highly challenging problem in imitation learning.
Meta-Model-Based Meta-Policy Optimization
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.
VILD: Variational Imitation Learning with Diverse-quality Demonstrations
However, the quality of demonstrations in reality can be diverse, since it is easier and cheaper to collect demonstrations from a mix of experts and amateurs.
Imitation Learning from Imperfect Demonstration
Imitation learning (IL) aims to learn an optimal policy from demonstrations.
Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization
However, identifying the hierarchical policy structure that enhances the performance of RL is not a trivial task.
TD-Regularized Actor-Critic Methods
Actor-critic methods can achieve incredible performance on difficult reinforcement learning problems, but they are also prone to instability.
Active Deep Q-learning with Demonstration
In this work, we propose Active Reinforcement Learning with Demonstration (ARLD), a new framework to streamline RL in terms of demonstration efforts by allowing the RL agent to query for demonstration actively during training.
Improving Generative Adversarial Imitation Learning with Non-expert Demonstrations
Imitation learning aims to learn an optimal policy from expert demonstrations and its recent combination with deep learning has shown impressive performance.
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
Uncertainty computation in deep learning is essential to design robust and reliable systems.
Vprop: Variational Inference using RMSprop
Overall, this paper presents Vprop as a principled, computationally-efficient, and easy-to-implement method for Bayesian deep learning.
Variational Adaptive-Newton Method for Explorative Learning
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 reinforcement learning.
Guide Actor-Critic for Continuous Control
First, we show that GAC updates the guide actor by performing second-order optimization in the action space where the curvature matrix is based on the Hessians of the critic.
Policy Search with High-Dimensional Context Variables
A naive application of unsupervised dimensionality reduction methods to the context variables, such as principal component analysis, is insufficient as task-relevant input may be ignored.
Direct Estimation of the Derivative of Quadratic Mutual Information with Application in Supervised Dimension Reduction
On the other hand, quadratic MI (QMI) is a variant of MI based on the $L_2$ distance which is more robust against outliers than the KL divergence, and a computationally efficient method to estimate QMI from data, called least-squares QMI (LSQMI), has been proposed recently.
Conditional Density Estimation with Dimensionality Reduction via Squared-Loss Conditional Entropy Minimization
In such a case, estimating the conditional density itself is preferable, but conditional density estimation (CDE) is challenging in high-dimensional space.
Model-Based Policy Gradients with Parameter-Based Exploration by Least-Squares Conditional Density Estimation
The model-free RL approach directly learns the policy based on data samples.
