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Found 17 papers, 13 papers with code
The BrowserGym Ecosystem for Web Agent Research
The BrowserGym ecosystem addresses the growing need for efficient evaluation and benchmarking of web agents, particularly those leveraging automation and Large Language Models (LLMs).
Learning Valid Dual Bounds in Constraint Programming: Boosted Lagrangian Decomposition with Self-Supervised Learning
Lagrangian decomposition (LD) is a relaxation method that provides a dual bound for constrained optimization problems by decomposing them into more manageable sub-problems.
MARCO: A Memory-Augmented Reinforcement Framework for Combinatorial Optimization
Neural Combinatorial Optimization (NCO) is an emerging domain where deep learning techniques are employed to address combinatorial optimization problems as a standalone solver.
WorkArena++: Towards Compositional Planning and Reasoning-based Common Knowledge Work Tasks
The ability of large language models (LLMs) to mimic human-like intelligence has led to a surge in LLM-based autonomous agents.
WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?
We study the use of large language model-based agents for interacting with software via web browsers.
Towards a Generic Representation of Combinatorial Problems for Learning-Based Approaches
In response to this challenge, this paper advocates for progress toward a fully generic representation of combinatorial problems for learning-based approaches.
Learning Lagrangian Multipliers for the Travelling Salesman Problem
Lagrangian relaxation is a versatile mathematical technique employed to relax constraints in an optimization problem, enabling the generation of dual bounds to prove the optimality of feasible solutions and the design of efficient propagators in constraint programming (such as the weighted circuit constraint).
Global Rewards in Multi-Agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems
We study vehicle dispatching in autonomous mobility on demand (AMoD) systems, where a central operator assigns vehicles to customer requests or rejects these with the aim of maximizing its total profit.
Learning a Generic Value-Selection Heuristic Inside a Constraint Programming Solver
Important design choices in a solver are the branching heuristics, which are designed to lead the search to the best solutions in a minimum amount of time.
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights
Combinatorial optimization is a well-established area in operations research and computer science.
On Causal Inference for Data-free Structured Pruning
Neural networks (NNs) are making a large impact both on research and industry.
SeaPearl: A Constraint Programming Solver guided by Reinforcement Learning
This paper presents the proof of concept for SeaPearl, a new CP solver implemented in Julia, that supports machine learning routines in order to learn branching decisions using reinforcement learning.
Combinatorial optimization and reasoning with graph neural networks
Combinatorial optimization is a well-established area in operations research and computer science.
Learning the Travelling Salesperson Problem Requires Rethinking Generalization
End-to-end training of neural network solvers for graph combinatorial optimization problems such as the Travelling Salesperson Problem (TSP) have seen a surge of interest recently, but remain intractable and inefficient beyond graphs with few hundreds of nodes.
Combining Reinforcement Learning and Constraint Programming for Combinatorial Optimization
In this work, we propose a general and hybrid approach, based on DRL and CP, for solving combinatorial optimization problems.
How to Evaluate Machine Learning Approaches for Combinatorial Optimization: Application to the Travelling Salesman Problem
In this paper, we address the limitations of ML approaches for solving the TSP and investigate two fundamental questions: (1) how can we measure the level of accuracy of the pure ML component of such methods; and (2) what is the impact of a search procedure plugged inside a ML model on the performances?
Improving Optimization Bounds using Machine Learning: Decision Diagrams meet Deep Reinforcement Learning
Finding tight bounds on the optimal solution is a critical element of practical solution methods for discrete optimization problems.
