
Generalization in Deep RL for TSP Problems via Equivariance and Local Search
Deep reinforcement learning (RL) has proved to be a competitive heuristi...
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Improving Generalization of Deep Reinforcement Learningbased TSP Solvers
Recent work applying deep reinforcement learning (DRL) to solve travelin...
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Learning Symbolic Rules for Interpretable Deep Reinforcement Learning
Recent progress in deep reinforcement learning (DRL) can be largely attr...
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Safe Distributional Reinforcement Learning
Safety in reinforcement learning (RL) is a key property in both training...
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Analytics and Machine Learning in Vehicle Routing Research
The Vehicle Routing Problem (VRP) is one of the most intensively studied...
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Learning Fair Policies in Multiobjective (Deep) Reinforcement Learning with Average and Discounted Rewards
As the operations of autonomous systems generally affect simultaneously ...
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Reinforcement Learning
Reinforcement learning (RL) is a general framework for adaptive control,...
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Towards More Sample Efficiency in Reinforcement Learning with Data Augmentation
Deep reinforcement learning (DRL) is a promising approach for adaptive r...
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Towards More Sample Efficiency inReinforcement Learning with Data Augmentation
Deep reinforcement learning (DRL) is a promising approach for adaptive r...
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Invariant Transform Experience Replay
Deep reinforcement learning (DRL) is a promising approach for adaptive r...
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Fairness in Reinforcement Learning
Decision support systems (e.g., for ecological conservation) and autonom...
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Exploiting the sign of the advantage function to learn deterministic policies in continuous domains
In the context of learning deterministic policies in continuous domains,...
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Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems
Social recommendation leverages social information to solve data sparsit...
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An Efficient PrimalDual Algorithm for Fair Combinatorial Optimization Problems
We consider a general class of combinatorial optimization problems inclu...
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Optimal Threshold Policies for Robust Data Center Control
With the simultaneous rise of energy costs and demand for cloud computin...
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From PreferenceBased to Multiobjective Sequential DecisionMaking
In this paper, we present a link between preferencebased and multiobjec...
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Finding RiskAverse Shortest Path with Timedependent Stochastic Costs
In this paper, we tackle the problem of riskaverse route planning in a ...
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Optimizing Quantiles in Preferencebased Markov Decision Processes
In the Markov decision process model, policies are usually evaluated by ...
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Quantile Reinforcement Learning
In reinforcement learning, the standard criterion to evaluate policies i...
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Approximation of LorenzOptimal Solutions in Multiobjective Markov Decision Processes
This paper is devoted to fair optimization in Multiobjective Markov Deci...
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Qualitative Decision Making Under Possibilistic Uncertainty: Toward more discriminating criteria
The aim of this paper is to propose a generalization of previous approac...
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Paul Weng
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