research
∙
03/16/2023
Learning Rewards to Optimize Global Performance Metrics in Deep Reinforcement Learning
When applying reinforcement learning (RL) to a new problem, reward engin...
share
research
∙
10/07/2021
Generalization in Deep RL for TSP Problems via Equivariance and Local Search
Deep reinforcement learning (RL) has proved to be a competitive heuristi...
research
∙
10/06/2021
Improving Generalization of Deep Reinforcement Learning-based TSP Solvers
Recent work applying deep reinforcement learning (DRL) to solve travelin...
research
∙
03/15/2021
Learning Symbolic Rules for Interpretable Deep Reinforcement Learning
Recent progress in deep reinforcement learning (DRL) can be largely attr...
research
∙
02/26/2021
Safe Distributional Reinforcement Learning
Safety in reinforcement learning (RL) is a key property in both training...
research
∙
02/19/2021
Analytics and Machine Learning in Vehicle Routing Research
The Vehicle Routing Problem (VRP) is one of the most intensively studied...
research
∙
08/18/2020
Learning Fair Policies in Multiobjective (Deep) Reinforcement Learning with Average and Discounted Rewards
As the operations of autonomous systems generally affect simultaneously ...
research
∙
05/29/2020
Reinforcement Learning
Reinforcement learning (RL) is a general framework for adaptive control,...
research
∙
10/19/2019
Towards More Sample Efficiency in Reinforcement Learning with Data Augmentation
Deep reinforcement learning (DRL) is a promising approach for adaptive r...
research
∙
10/19/2019
Towards More Sample Efficiency inReinforcement Learning with Data Augmentation
Deep reinforcement learning (DRL) is a promising approach for adaptive r...
research
∙
09/24/2019
Invariant Transform Experience Replay
Deep reinforcement learning (DRL) is a promising approach for adaptive r...
research
∙
07/24/2019
Fairness in Reinforcement Learning
Decision support systems (e.g., for ecological conservation) and autonom...
research
∙
06/10/2019
Exploiting the sign of the advantage function to learn deterministic policies in continuous domains
In the context of learning deterministic policies in continuous domains,...
research
∙
03/25/2019
Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems
Social recommendation leverages social information to solve data sparsit...
research
∙
01/23/2018
An Efficient Primal-Dual Algorithm for Fair Combinatorial Optimization Problems
We consider a general class of combinatorial optimization problems inclu...
research
∙
08/23/2017
Optimal Threshold Policies for Robust Data Center Control
With the simultaneous rise of energy costs and demand for cloud computin...
research
∙
01/03/2017
From Preference-Based to Multiobjective Sequential Decision-Making
In this paper, we present a link between preference-based and multiobjec...
research
∙
01/03/2017
Finding Risk-Averse Shortest Path with Time-dependent Stochastic Costs
In this paper, we tackle the problem of risk-averse route planning in a ...
research
∙
12/01/2016
Optimizing Quantiles in Preference-based Markov Decision Processes
In the Markov decision process model, policies are usually evaluated by ...
research
∙
11/03/2016
Quantile Reinforcement Learning
In reinforcement learning, the standard criterion to evaluate policies i...
research
∙
09/26/2013
Approximation of Lorenz-Optimal Solutions in Multiobjective Markov Decision Processes
This paper is devoted to fair optimization in Multiobjective Markov Deci...
research
∙
07/04/2012
