Search Results for author:
Found 9 papers, 1 papers with code
Individually Rational Collaborative Vehicle Routing through Give-And-Take Exchanges
In this paper, we are concerned with the automated exchange of orders between logistics companies in a marketplace platform to optimize total revenues.
The BeMi Stardust: a Structured Ensemble of Binarized Neural Networks
While the previous approaches achieve an average accuracy of 51. 1% on the MNIST dataset, the BeMi ensemble achieves an average accuracy of 61. 7% when trained with 10 images per class and 76. 4% when trained with 40 images per class.
Online Control of Adaptive Large Neighborhood Search using Deep Reinforcement Learning
To address this, we introduce a Deep Reinforcement Learning (DRL) based approach called DR-ALNS that selects operators, adjusts parameters, and controls the acceptance criterion throughout the search.
QROSS: QUBO Relaxation Parameter Optimisation via Learning Solver Surrogates
In this way, we are able capture the common structure of the instances and their interactions with the solver, and produce good choices of penalty parameters with fewer number of calls to the QUBO solver.
Credit Assignment For Collective Multiagent RL With Global Rewards
Scaling decision theoretic planning to large multiagent systems is challenging due to uncertainty and partial observability in the environment.
Policy Gradient With Value Function Approximation For Collective Multiagent Planning
Decentralized (PO)MDPs provide an expressive framework for sequential decision making in a multiagent system.
Entropy based Independent Learning in Anonymous Multi-Agent Settings
A key characteristic of the domains of interest is that the interactions between individuals are anonymous, i. e., the outcome of an interaction (competing for demand) is dependent only on the number and not on the identity of the agents.
Local Gaussian Processes for Efficient Fine-Grained Traffic Speed Prediction
In this work, we address their efficiency issues by proposing local GPs to learn from and make predictions for correlated subsets of data.
Robust Local Search for Solving RCPSP/max with Durational Uncertainty
Thus, in this paper, our focus is on providing a scalable method for solving RCPSP/max problems with durational uncertainty.
