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Found 14 papers, 2 papers with code
A Graph Neural Network-Based QUBO-Formulated Hamiltonian-Inspired Loss Function for Combinatorial Optimization using Reinforcement Learning
We formulate and empirically evaluate the compatibility of the QUBO-formulated Hamiltonian as the generic reward function in the RL-based paradigm in the form of rewards.
DePAint: A Decentralized Safe Multi-Agent Reinforcement Learning Algorithm considering Peak and Average Constraints
In this paper, we address the problem of multi-agent policy optimization in a decentralized setting, where agents communicate with their neighbors to maximize the sum of their cumulative rewards while also satisfying each agent's safety constraints.
A Graph Neural Network-Based QUBO-Formulated Hamiltonian-Inspired Loss Function for Combinatorial Optimization using Reinforcement Learning
The Hamiltonian function is often used to formulate QUBO problems where it is used as the objective function in the context of optimization.
Improving Causal Effect Estimation of Weighted RegressionBased Estimator using Neural Networks
Estimating causal effects from observational data informs us about which factors are important in an autonomous system, and enables us to take better decisions.
Learning Cooperation and Online Planning Through Simulation and Graph Convolutional Network
Against this background, we introduce a simulation based online planning algorithm, that we call SiCLOP, for multi-agent cooperative environments.
Accelerating Recursive Partition-Based Causal Structure Learning
This paper proposes a generic causal structure refinement strategy that can locate the undesired relations with a small number of CI-tests, thus speeding up the algorithm for large and complex problems.
Improving Solution Quality of Bounded Max-Sum Algorithm to Solve DCOPs involving Hard and Soft Constraints
In particular, BMS algorithm is able to solve problems of this type having large search space at the expense of low computational cost.
A Particle Swarm Inspired Approach for Continuous Distributed Constraint Optimization Problems
To address this issue, we propose a new C-DCOP algorithm, namely Particle Swarm Optimization Based C-DCOP (PCD), which is inspired by Particle Swarm Optimization (PSO), a well-known centralized population-based approach for solving continuous optimization problems.
On Population-Based Algorithms for Distributed Constraint Optimization Problems
The main characteristic of these algorithms is that they maintain a population of candidate solutions of a given problem and use this population to cover a large area of the search space and to avoid local-optima.
C-CoCoA: A Continuous Cooperative Constraint Approximation Algorithm to Solve Functional DCOPs
To overcome this limitation, Functional DCOP (F-DCOP) model is proposed that is able to model problems with continuous variables.
Learning Optimal Temperature Region for Solving Mixed Integer Functional DCOPs
Distributed Constraint Optimization Problems (DCOPs) are an important framework for modeling coordinated decision-making problems in multi-agent systems with a set of discrete variables.
Speeding Up Distributed Pseudo-tree Optimization Procedure with Cross Edge Consistency to Solve DCOPs
Distributed Pseudo-tree Optimization Procedure (DPOP) is a well-known message passing algorithm that has been used to provide optimal solutions of Distributed Constraint Optimization Problems (DCOPs) -- a framework that is designed to optimize constraints in cooperative multi-agent systems.
AED: An Anytime Evolutionary DCOP Algorithm
Evolutionary optimization is a generic population-based metaheuristic that can be adapted to solve a wide variety of optimization problems and has proven very effective for combinatorial optimization problems.
A Gray Box Interpretable Visual Debugging Approach for Deep Sequence Learning Model
In this work, we have developed a visual interactive web application, namely d-DeVIS, which helps to visualize the internal reasoning of the learning model which is trained on the audio data.
