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09/11/2023
Learning Objective-Specific Active Learning Strategies with Attentive Neural Processes
Pool-based active learning (AL) is a promising technology for increasing...
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02/07/2023
Uncoupled Learning of Differential Stackelberg Equilibria with Commitments
A natural solution concept for many multiagent settings is the Stackelbe...
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12/22/2022
Reusable Options through Gradient-based Meta Learning
Hierarchical methods in reinforcement learning have the potential to red...
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09/04/2022
Exposure-Aware Recommendation using Contextual Bandits
Exposure bias is a well-known issue in recommender systems where items a...
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08/20/2022
Calculus on MDPs: Potential Shaping as a Gradient
In reinforcement learning, different reward functions can be equivalent ...
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07/13/2022
Neural Topological Ordering for Computation Graphs
Recent works on machine learning for combinatorial optimization have sho...
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03/08/2022
Logic-based AI for Interpretable Board Game Winner Prediction with Tsetlin Machine
Hex is a turn-based two-player connection game with a high branching fac...
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03/07/2022
Reliably Re-Acting to Partner's Actions with the Social Intrinsic Motivation of Transfer Empowerment
We consider multi-agent reinforcement learning (MARL) for cooperative co...
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03/07/2022
Fast and Data Efficient Reinforcement Learning from Pixels via Non-Parametric Value Approximation
We present Nonparametric Approximation of Inter-Trace returns (NAIT), a ...
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01/28/2022
Leveraging class abstraction for commonsense reinforcement learning via residual policy gradient methods
Enabling reinforcement learning (RL) agents to leverage a knowledge base...
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10/09/2021
Multi-Agent MDP Homomorphic Networks
This paper introduces Multi-Agent MDP Homomorphic Networks, a class of n...
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09/23/2021
Hierarchies of Planning and Reinforcement Learning for Robot Navigation
Solving robotic navigation tasks via reinforcement learning (RL) is chal...
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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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03/22/2021
Combining Reward Information from Multiple Sources
Given two sources of evidence about a latent variable, one can combine t...
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02/23/2021
Deep Policy Dynamic Programming for Vehicle Routing Problems
Routing problems are a class of combinatorial problems with many practic...
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02/16/2021
Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models
Reinforcement learning is a promising paradigm for solving sequential de...
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10/30/2020
Experimental design for MRI by greedy policy search
In today's clinical practice, magnetic resonance imaging (MRI) is routin...
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08/21/2020
Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables
Neural processes (NPs) constitute a family of variational approximate mo...
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07/03/2020
An Autonomous Free Airspace En-route Controller using Deep Reinforcement Learning Techniques
Air traffic control is becoming a more and more complex task due to the ...
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06/30/2020
MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning
This paper introduces MDP homomorphic networks for deep reinforcement le...
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03/18/2020
Social navigation with human empowerment driven reinforcement learning
The next generation of mobile robots needs to be socially-compliant to b...
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02/14/2020
Estimating Gradients for Discrete Random Variables by Sampling without Replacement
We derive an unbiased estimator for expectations over discrete random va...
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10/23/2019
Unifying Variational Inference and PAC-Bayes for Supervised Learning that Scales
Neural Network based controllers hold enormous potential to learn comple...
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06/15/2019
Reinforcement Learning with Non-uniform State Representations for Adaptive Search
Efficient spatial exploration is a key aspect of search and rescue. In t...
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03/14/2019
Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement
The well-known Gumbel-Max trick for sampling from a categorical distribu...
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03/13/2019
Uncertainty Aware Learning from Demonstrations in Multiple Contexts using Bayesian Neural Networks
Diversity of environments is a key challenge that causes learned robotic...
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12/04/2018
Deep Generative Modeling of LiDAR Data
Building models capable of generating structured output is a key challen...
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09/25/2018
BanditSum: Extractive Summarization as a Contextual Bandit
In this work, we propose a novel method for training neural networks to ...
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02/26/2018
Addressing Function Approximation Error in Actor-Critic Methods
In value-based reinforcement learning methods such as deep Q-learning, f...
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11/10/2016
