C-CoCoA: A Continuous Cooperative Constraint Approximation Algorithm to Solve Functional DCOPs

• URL: http://arxiv.org/abs/2002.12427v1
• Date: Thu, 27 Feb 2020 20:44:25 GMT
• Title: C-CoCoA: A Continuous Cooperative Constraint Approximation Algorithm to Solve Functional DCOPs
• Authors: Amit Sarker, Abdullahil Baki Arif, Moumita Choudhury, Md. Mosaddek Khan
• Abstract summary: This paper applies continuous non-linear optimization methods on Cooperative Constraint Approximation (CoCoA) algorithm. Our algorithm is able to provide high-quality solutions at the expense of smaller communication cost and execution time.
• Score: 4.404507236193031
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Distributed Constraint Optimization Problems (DCOPs) have been widely used to coordinate interactions (i.e. constraints) in cooperative multi-agent systems. The traditional DCOP model assumes that variables owned by the agents can take only discrete values and constraints' cost functions are defined for every possible value assignment of a set of variables. While this formulation is often reasonable, there are many applications where the variables are continuous decision variables and constraints are in functional form. To overcome this limitation, Functional DCOP (F-DCOP) model is proposed that is able to model problems with continuous variables. The existing F-DCOPs algorithms experience huge computation and communication overhead. This paper applies continuous non-linear optimization methods on Cooperative Constraint Approximation (CoCoA) algorithm. We empirically show that our algorithm is able to provide high-quality solutions at the expense of smaller communication cost and execution time compared to the existing F-DCOP algorithms.

Related papers

• Sample and Oracle Efficient Reinforcement Learning for MDPs with Linearly-Realizable Value Functions [10.225358400539719]
  We present an efficient reinforcement algorithm for Decision (MDPs) where a linear-action-action generalizes in a feature map. Specifically, we introduce a new algorithm that efficiently finds a near-optimal policy in this setting.
  arXiv  Detail & Related papers  (2024-09-07T14:38:05Z)
• Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets [86.43523688236077]
  Combinatorial optimization (CO) problems are often NP-hard and out of reach for exact algorithms. GFlowNets have emerged as a powerful machinery to efficiently sample from composite unnormalized densities sequentially. In this paper, we design Markov decision processes (MDPs) for different problems and propose to train conditional GFlowNets to sample from the solution space.
  arXiv  Detail & Related papers  (2023-05-26T15:13:09Z)
• Symmetric Tensor Networks for Generative Modeling and Constrained Combinatorial Optimization [72.41480594026815]
  Constrained optimization problems abound in industry, from portfolio optimization to logistics. One of the major roadblocks in solving these problems is the presence of non-trivial hard constraints which limit the valid search space. In this work, we encode arbitrary integer-valued equality constraints of the form Ax=b, directly into U(1) symmetric networks (TNs) and leverage their applicability as quantum-inspired generative models.
  arXiv  Detail & Related papers  (2022-11-16T18:59:54Z)
• RACA: Relation-Aware Credit Assignment for Ad-Hoc Cooperation in Multi-Agent Deep Reinforcement Learning [55.55009081609396]
  We propose a novel method, called Relation-Aware Credit Assignment (RACA), which achieves zero-shot generalization in ad-hoc cooperation scenarios. RACA takes advantage of a graph-based encoder relation to encode the topological structure between agents. Our method outperforms baseline methods on the StarCraftII micromanagement benchmark and ad-hoc cooperation scenarios.
  arXiv  Detail & Related papers  (2022-06-02T03:39:27Z)
• Stabilizing Q-learning with Linear Architectures for Provably Efficient Learning [53.17258888552998]
  This work proposes an exploration variant of the basic $Q$-learning protocol with linear function approximation. We show that the performance of the algorithm degrades very gracefully under a novel and more permissive notion of approximation error.
  arXiv  Detail & Related papers  (2022-06-01T23:26:51Z)
• Improving Solution Quality of Bounded Max-Sum Algorithm to Solve DCOPs involving Hard and Soft Constraints [3.0969191504482243]
  Bounded Max-Sum (BMS) is a message-passing algorithm that provides approximation solution to a specific form of de-centralized coordination problems. In particular, BMS algorithm is able to solve problems of this type having large search space at the expense of low computational cost.
  arXiv  Detail & Related papers  (2020-12-02T18:10:14Z)
• Efficient Consensus Model based on Proximal Gradient Method applied to Convolutional Sparse Problems [2.335152769484957]
  We derive and detail a theoretical analysis of an efficient consensus algorithm based on gradient proximal (PG) approach. The proposed algorithm is also applied to another particular convolutional problem for the anomaly detection task.
  arXiv  Detail & Related papers  (2020-11-19T20:52:48Z)
• A Particle Swarm Inspired Approach for Continuous Distributed Constraint Optimization Problems [7.512486812178571]
  Continuous DCOPs can explicitly model problems with continuous variables. State-of-the-art approaches for solving C-DCOPs experience either onerous memory or computation overhead. We propose a new C-DCOP algorithm, namely Particle Swarm Optimization Based C-DCOP (PCD), which is inspired by Particle Swarm Optimization (PSO)
  arXiv  Detail & Related papers  (2020-10-20T11:04:47Z)
• Combining Deep Learning and Optimization for Security-Constrained Optimal Power Flow [94.24763814458686]
  Security-constrained optimal power flow (SCOPF) is fundamental in power systems. Modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs. This paper proposes a novel approach that combines deep learning and robust optimization techniques.
  arXiv  Detail & Related papers  (2020-07-14T12:38:21Z)
• Conditional gradient methods for stochastically constrained convex minimization [54.53786593679331]
  We propose two novel conditional gradient-based methods for solving structured convex optimization problems. The most important feature of our framework is that only a subset of the constraints is processed at each iteration. Our algorithms rely on variance reduction and smoothing used in conjunction with conditional gradient steps, and are accompanied by rigorous convergence guarantees.
  arXiv  Detail & Related papers  (2020-07-07T21:26:35Z)
• Learning Optimal Temperature Region for Solving Mixed Integer Functional DCOPs [26.16778095954815]
  We combine Distributed Constraint Optimization Problems (DCOPs) with Functional DCOPs (F-DCOPs) We then propose a novel algorithm $-$ Distributed Parallel Simulated Annealing (DPSA) We show that DPSA produces solutions of significantly better quality than the state-of-the-art non-exact algorithms in their corresponding settings.
  arXiv  Detail & Related papers  (2020-02-27T09:46:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.