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08/29/2023
Policy composition in reinforcement learning via multi-objective policy optimization
We enable reinforcement learning agents to learn successful behavior pol...
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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...
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06/04/2023
For SALE: State-Action Representation Learning for Deep Reinforcement Learning
In the field of reinforcement learning (RL), representation learning is ...
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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...
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05/09/2023
Policy Gradient Methods in the Presence of Symmetries and State Abstractions
Reinforcement learning on high-dimensional and complex problems relies o...
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04/28/2023
MUDiff: Unified Diffusion for Complete Molecule Generation
We present a new model for generating molecular data by combining discre...
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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 ...
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03/31/2023
Accelerating exploration and representation learning with offline pre-training
Sequential decision-making agents struggle with long horizon tasks, sinc...
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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 ...
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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...
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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...
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12/29/2022
Offline Policy Optimization in RL with Variance Regularizaton
Learning policies from fixed offline datasets is a key challenge to scal...
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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...
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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...
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11/22/2022
Simulating Human Gaze with Neural Visual Attention
Existing models of human visual attention are generally unable to incorp...
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11/06/2022
On learning history based policies for controlling Markov decision processes
Reinforcementlearning(RL)folkloresuggeststhathistory-basedfunctionapprox...
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10/30/2022
When Do We Need GNN for Node Classification?
Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by addit...
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10/14/2022
Revisiting Heterophily For Graph Neural Networks
Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by using...
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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...
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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...
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10/01/2022
Bayesian Q-learning With Imperfect Expert Demonstrations
Guided exploration with expert demonstrations improves data efficiency f...
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09/15/2022
Continuous MDP Homomorphisms and Homomorphic Policy Gradient
Abstraction has been widely studied as a way to improve the efficiency a...
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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...
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04/21/2022
Learning how to Interact with a Complex Interface using Hierarchical Reinforcement Learning
Hierarchical Reinforcement Learning (HRL) allows interactive agents to d...
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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 ...
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04/19/2022
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation
We consider the offline constrained reinforcement learning (RL) problem,...
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04/11/2022
Towards Painless Policy Optimization for Constrained MDPs
We study policy optimization in an infinite horizon, γ-discounted constr...
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02/20/2022
Selective Credit Assignment
Efficient credit assignment is essential for reinforcement learning algo...
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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...
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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...
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01/24/2022
The Paradox of Choice: Using Attention in Hierarchical Reinforcement Learning
Decision-making AI agents are often faced with two important challenges:...
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01/07/2022
Attention Option-Critic
Temporal abstraction in reinforcement learning is the ability of an agen...
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12/31/2021
Single-Shot Pruning for Offline Reinforcement Learning
Deep Reinforcement Learning (RL) is a powerful framework for solving com...
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12/31/2021
Importance of Empirical Sample Complexity Analysis for Offline Reinforcement Learning
We hypothesize that empirically studying the sample complexity of offlin...
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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...
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12/20/2021
Proving Theorems using Incremental Learning and Hindsight Experience Replay
Traditional automated theorem provers for first-order logic depend on sp...
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12/06/2021
Flexible Option Learning
Temporal abstraction in reinforcement learning (RL), offers the promise ...
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11/01/2021
On the Expressivity of Markov Reward
Reward is the driving force for reinforcement-learning agents. This pape...
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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...
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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 ...
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09/01/2021
A Survey of Exploration Methods in Reinforcement Learning
Exploration is an essential component of reinforcement learning algorith...
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08/06/2021
Temporally Abstract Partial Models
Humans and animals have the ability to reason and make predictions about...
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08/04/2021
Policy Gradients Incorporating the Future
Reasoning about the future – understanding how decisions in the present ...
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06/15/2021
Randomized Exploration for Reinforcement Learning with General Value Function Approximation
We propose a model-free reinforcement learning algorithm inspired by the...
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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...
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06/11/2021
Preferential Temporal Difference Learning
Temporal-Difference (TD) learning is a general and very useful tool for ...
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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...
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06/07/2021
Correcting Momentum in Temporal Difference Learning
A common optimization tool used in deep reinforcement learning is moment...
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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 ...
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06/01/2021