b'Doina Precup'

research

∙ 08/29/2023

Policy composition in reinforcement learning via multi-objective policy optimization

We enable reinforcement learning agents to learn successful behavior pol...

0 Shruti Mishra, et al. ∙

research

∙ 07/15/2023

An Empirical Study of the Effectiveness of Using a Replay Buffer on Mode Discovery in GFlowNets

Reinforcement Learning (RL) algorithms aim to learn an optimal policy by...

0 Nikhil Vemgal, et al. ∙

research

∙ 06/04/2023

For SALE: State-Action Representation Learning for Deep Reinforcement Learning

In the field of reinforcement learning (RL), representation learning is ...

0 Scott Fujimoto, et al. ∙

research

∙ 05/29/2023

Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo

We present a scalable and effective exploration strategy based on Thomps...

3 Haque Ishfaq, et al. ∙

research

∙ 05/09/2023

Policy Gradient Methods in the Presence of Symmetries and State Abstractions

Reinforcement learning on high-dimensional and complex problems relies o...

0 Prakash Panangaden, et al. ∙

research

∙ 04/28/2023

MUDiff: Unified Diffusion for Complete Molecule Generation

We present a new model for generating molecular data by combining discre...

11 Chenqing Hua, et al. ∙

research

∙ 04/25/2023

When Do Graph Neural Networks Help with Node Classification: Investigating the Homophily Principle on Node Distinguishability

Homophily principle, i.e. nodes with the same labels are more likely to ...

27 Sitao Luan, et al. ∙

research

∙ 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. ∙

research

∙ 02/14/2023

The Stable Entropy Hypothesis and Entropy-Aware Decoding: An Analysis and Algorithm for Robust Natural Language Generation

State-of-the-art language generation models can degenerate when applied ...

0 Kushal Arora, et al. ∙

research

∙ 01/24/2023

Minimal Value-Equivalent Partial Models for Scalable and Robust Planning in Lifelong Reinforcement Learning

Learning models of the environment from pure interaction is often consid...

0 Safa Alver, et al. ∙

research

∙ 01/02/2023

On the Challenges of using Reinforcement Learning in Precision Drug Dosing: Delay and Prolongedness of Action Effects

Drug dosing is an important application of AI, which can be formulated a...

0 Sumana Basu, et al. ∙

research

∙ 12/29/2022

Offline Policy Optimization in RL with Variance Regularizaton

Learning policies from fixed offline datasets is a key challenge to scal...

0 Riashat Islam, et al. ∙

research

∙ 12/21/2022

Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Neural Networks

The core operation of current Graph Neural Networks (GNNs) is the aggreg...

0 Sitao Luan, et al. ∙

research

∙ 11/23/2022

Multi-Environment Pretraining Enables Transfer to Action Limited Datasets

Using massive datasets to train large-scale models has emerged as a domi...

0 David Venuto, et al. ∙

research

∙ 11/22/2022

Simulating Human Gaze with Neural Visual Attention

Existing models of human visual attention are generally unable to incorp...

0 Leo Schwinn, et al. ∙

research

∙ 11/06/2022

On learning history based policies for controlling Markov decision processes

Reinforcementlearning(RL)folkloresuggeststhathistory-basedfunctionapprox...

0 Gandharv Patil, et al. ∙

research

∙ 10/30/2022

When Do We Need GNN for Node Classification?

Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by addit...

1 Sitao Luan, et al. ∙

research

∙ 10/14/2022

Revisiting Heterophily For Graph Neural Networks

Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by using...

27 Sitao Luan, et al. ∙

research

∙ 10/12/2022

Finite time analysis of temporal difference learning with linear function approximation: Tail averaging and regularisation

We study the finite-time behaviour of the popular temporal difference (T...

0 Gandharv Patil, et al. ∙

research

∙ 10/05/2022

Towards Safe Mechanical Ventilation Treatment Using Deep Offline Reinforcement Learning

Mechanical ventilation is a key form of life support for patients with p...

0 Flemming Kondrup, et al. ∙

research

∙ 10/01/2022

Bayesian Q-learning With Imperfect Expert Demonstrations

Guided exploration with expert demonstrations improves data efficiency f...

0 Fengdi Che, et al. ∙

research

∙ 09/15/2022

Continuous MDP Homomorphisms and Homomorphic Policy Gradient

Abstraction has been widely studied as a way to improve the efficiency a...

5 Sahand Rezaei-Shoshtari, et al. ∙

research

∙ 06/16/2022

Understanding Decision-Time vs. Background Planning in Model-Based Reinforcement Learning

In model-based reinforcement learning, an agent can leverage a learned m...

0 Safa Alver, et al. ∙

research

∙ 04/21/2022

Learning how to Interact with a Complex Interface using Hierarchical Reinforcement Learning

Hierarchical Reinforcement Learning (HRL) allows interactive agents to d...

0 Gheorghe Comanici, et al. ∙

research

∙ 04/19/2022

Behind the Machine's Gaze: Biologically Constrained Neural Networks Exhibit Human-like Visual Attention

By and large, existing computational models of visual attention tacitly ...

0 Leo Schwinn, et al. ∙

research

∙ 04/19/2022

COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation

We consider the offline constrained reinforcement learning (RL) problem,...

0 Jongmin Lee, et al. ∙

research

∙ 04/11/2022

Towards Painless Policy Optimization for Constrained MDPs

We study policy optimization in an infinite horizon, γ-discounted constr...

2 Arushi Jain, et al. ∙

research

∙ 02/20/2022

Selective Credit Assignment

Efficient credit assignment is essential for reinforcement learning algo...

0 Veronica Chelu, et al. ∙

research

∙ 02/01/2022

Improving Sample Efficiency of Value Based Models Using Attention and Vision Transformers

Much of recent Deep Reinforcement Learning success is owed to the neural...

0 Amir Ardalan Kalantari, et al. ∙

research

∙ 01/28/2022

Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error

In this work, we study the use of the Bellman equation as a surrogate ob...

0 Scott Fujimoto, et al. ∙

research

∙ 01/24/2022

The Paradox of Choice: Using Attention in Hierarchical Reinforcement Learning

Decision-making AI agents are often faced with two important challenges:...

0 Andrei Nica, et al. ∙

research

∙ 01/07/2022

Attention Option-Critic

Temporal abstraction in reinforcement learning is the ability of an agen...

10 Raviteja Chunduru, et al. ∙

research

∙ 12/31/2021

Single-Shot Pruning for Offline Reinforcement Learning

Deep Reinforcement Learning (RL) is a powerful framework for solving com...

1 Samin Yeasar Arnob, et al. ∙

research

∙ 12/31/2021

Importance of Empirical Sample Complexity Analysis for Offline Reinforcement Learning

We hypothesize that empirically studying the sample complexity of offlin...

0 Samin Yeasar Arnob, et al. ∙

research

∙ 12/30/2021

Constructing a Good Behavior Basis for Transfer using Generalized Policy Updates

We study the problem of learning a good set of policies, so that when co...

0 Safa Alver, et al. ∙

research

∙ 12/20/2021

Proving Theorems using Incremental Learning and Hindsight Experience Replay

Traditional automated theorem provers for first-order logic depend on sp...

0 Eser Aygün, et al. ∙

research

∙ 12/06/2021

Flexible Option Learning

Temporal abstraction in reinforcement learning (RL), offers the promise ...

5 Martin Klissarov, et al. ∙

research

∙ 11/01/2021

On the Expressivity of Markov Reward

Reward is the driving force for reinforcement-learning agents. This pape...

15 David Abel, et al. ∙

research

∙ 09/12/2021

Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?

Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by using...

39 Sitao Luan, et al. ∙

research

∙ 09/08/2021

Where Did You Learn That From? Surprising Effectiveness of Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning

While significant research advances have been made in the field of deep ...

28 Maziar Gomrokchi, et al. ∙

research

∙ 09/01/2021

A Survey of Exploration Methods in Reinforcement Learning

Exploration is an essential component of reinforcement learning algorith...

0 Susan Amin, et al. ∙

research

∙ 08/06/2021

Temporally Abstract Partial Models

Humans and animals have the ability to reason and make predictions about...

0 Khimya Khetarpal, et al. ∙

research

∙ 08/04/2021

Policy Gradients Incorporating the Future

Reasoning about the future – understanding how decisions in the present ...

0 David Venuto, et al. ∙

research

∙ 06/15/2021

Randomized Exploration for Reinforcement Learning with General Value Function Approximation

We propose a model-free reinforcement learning algorithm inspired by the...

0 Haque Ishfaq, et al. ∙

research

∙ 06/12/2021

A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor Representation

Marginalized importance sampling (MIS), which measures the density ratio...

0 Scott Fujimoto, et al. ∙

research

∙ 06/11/2021

Preferential Temporal Difference Learning

Temporal-Difference (TD) learning is a general and very useful tool for ...

17 Nishanth Anand, et al. ∙

research

∙ 06/08/2021

Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation

This paper is about the problem of learning a stochastic policy for gene...

11 Emmanuel Bengio, et al. ∙

research

∙ 06/07/2021

Correcting Momentum in Temporal Difference Learning

A common optimization tool used in deep reinforcement learning is moment...

26 Emmanuel Bengio, et al. ∙

research

∙ 06/03/2021

A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning

We present an end-to-end, model-based deep reinforcement learning agent ...

33 Mingde Zhao, et al. ∙

research

∙ 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. ∙

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