Bogdan Mazoure | DeepAI

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∙ 06/09/2023

Value function estimation using conditional diffusion models for control

A fairly reliable trend in deep reinforcement learning is that the perfo...

0 Bogdan Mazoure, et al. ∙

∙ 03/31/2023

Accelerating exploration and representation learning with offline pre-training

Sequential decision-making agents struggle with long horizon tasks, sinc...

0 Bogdan Mazoure, et al. ∙

∙ 11/03/2022

Contrastive Value Learning: Implicit Models for Simple Offline RL

Model-based reinforcement learning (RL) methods are appealing in the off...

0 Bogdan Mazoure, et al. ∙

∙ 06/08/2022

Sequential Density Estimation via NCWFAs Sequential Density Estimation via Nonlinear Continuous Weighted Finite Automata

Weighted finite automata (WFAs) have been widely applied in many fields....

0 Tianyu Li, et al. ∙

∙ 03/19/2022

The Sandbox Environment for Generalizable Agent Research (SEGAR)

A broad challenge of research on generalization for sequential decision-...

0 R Devon Hjelm, et al. ∙

∙ 11/29/2021

Improving Zero-shot Generalization in Offline Reinforcement Learning using Generalized Similarity Functions

Reinforcement learning (RL) agents are widely used for solving complex s...

0 Bogdan Mazoure, et al. ∙

∙ 06/04/2021

Cross-Trajectory Representation Learning for Zero-Shot Generalization in RL

A highly desirable property of a reinforcement learning (RL) agent – and...

0 Bogdan Mazoure, et al. ∙

∙ 06/01/2021

Improving Long-Term Metrics in Recommendation Systems using Short-Horizon Offline RL

We study session-based recommendation scenarios where we want to recomme...

1 Bogdan Mazoure, et al. ∙

∙ 10/07/2020

A Theoretical Analysis of Catastrophic Forgetting through the NTK Overlap Matrix

Continual learning (CL) is a setting in which an agent has to learn from...

0 Thang Doan, et al. ∙

∙ 06/12/2020

Deep Reinforcement and InfoMax Learning

Our work is based on the hypothesis that a model-free agent whose repres...

0 Bogdan Mazoure, et al. ∙

∙ 02/07/2020

Provably efficient reconstruction of policy networks

Recent research has shown that learning poli-cies parametrized by large ...

18 Bogdan Mazoure, et al. ∙

∙ 11/12/2019

Efficient Planning under Partial Observability with Unnormalized Q Functions and Spectral Learning

Learning and planning in partially-observable domains is one of the most...

0 Tianyu Li, et al. ∙

∙ 09/17/2019

Attraction-Repulsion Actor-Critic for Continuous Control Reinforcement Learning

Continuous control tasks in reinforcement learning are important because...

15 Thang Doan, et al. ∙

∙ 06/06/2019

Learning Gaussian Graphical Models with Ordered Weighted L1 Regularization

We address the task of identifying densely connected subsets of multivar...

0 Cody Mazza-Anthony, et al. ∙

∙ 05/16/2019

Leveraging exploration in off-policy algorithms via normalizing flows

Exploration is a crucial component for discovering approximately optimal...

0 Bogdan Mazoure, et al. ∙

∙ 02/01/2019

Exploring attention mechanism for acoustic-based classification of speech utterances into system-directed and non-system-directed

Voice controlled virtual assistants (VAs) are now available in smartphon...

0 Atta Norouzian, et al. ∙

∙ 07/31/2018

Online Adaptative Curriculum Learning for GANs

Generative Adversarial Networks (GANs) can successfully learn a probabil...

20 Thang Doan, et al. ∙

∙ 05/13/2018

GAN Q-learning

Distributional reinforcement learning (distributional RL) has seen empir...

0 Thang Doan, et al. ∙

∙ 11/12/2017

Alpha-Divergences in Variational Dropout

We investigate the use of alternative divergences to Kullback-Leibler (K...

0 Bogdan Mazoure, et al. ∙

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