Summary SeaPearl A Constraint Programming Solver with Reinforcement Learning arxiv.org
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SeaPearl is a hybrid solver that combines constraint programming with deep reinforcement learning to solve combinatorial optimization problems, with promising results and the ability for users to redefine various components for prototyping new research ideas.
Key Points
- SeaPearl is a solver that combines constraint programming and reinforcement learning to solve combinatorial optimization problems.
- SeaPearl uses reinforcement learning algorithms to find a policy that maximizes the total return.
- SeaPearl is fully implemented in Julia language and is available on Github.
- SeaPearl can handle any CP model and is trained using randomly generated instances.
- SeaPearl has shown promising results on several benchmark problems.
Summaries
323 word summary
SeaPearl is a hybrid solver that combines constraint programming with deep reinforcement learning to solve combinatorial optimization problems, reducing inference costs and enabling accurate predictions. The tool draws on probability distributions, optimization algorithms, and graph convolutional neural networks, and is built on top of the Gecode constraint programming library, with a focus on graph coloring and scheduling. The solver has shown promising results on several benchmark problems.
Future work proposed for SeaPearl includes having two specialized agents, one to find good solutions and one to prove optimality, and finding ways to prioritize these tasks, as well as extending the learning to variable selection and selecting an appropriate neural network architecture.
Experiments evaluate the ability of SeaPearl to learn good heuristics for value-selection, generating instances randomly with a custom generator. Comparisons against greedy heuristics on two NP-hard problems are proposed.
SeaPearl allows the user to redefine types without requiring changes to the source code, and many other components, such as the reward or the state representation, can be redefined by the end-user for prototyping new research ideas. SeaPearl is a new constraint programming solver that uses reinforcement learning to solve combinatorial optimization problems. The solver is implemented in Julia language and includes a learning component that uses reinforcement learning to guide the search process. SeaPearl aims to leverage historical data related to specific problems in order to solve future instances more quickly. The hybridization of constraint programming and machine learning is challenging to build, but SeaPearl demonstrates that reinforcement learning can be successfully used to drive the search process. SeaPearl uses a deep architecture consisting of 4 graph attention layers and 5 fully-connected layers, which can be easily defined using the library Flux. The solver can be used to solve the graph coloring problem, where the goal is to assign labels to each node such that adjacent nodes have a different label. SeaPearl is fully implemented in Julia language and is available on Github.
816 word summary
SeaPearl is a new solver that combines constraint programming and reinforcement learning to efficiently solve combinatorial optimization problems. The solver is implemented in Julia language and includes a learning component that uses reinforcement learning to guide the search process. SeaPearl aims to leverage historical data related to specific problems in order to solve future instances more quickly. The hybridization of constraint programming and machine learning is challenging to build, but SeaPearl demonstrates that reinforcement learning can be successfully used to drive the search process. The goal of combinatorial optimization is to find an optimal solution among a finite set of possibilities. SeaPearl uses reinforcement learning algorithms to find a policy that maximizes the total return, which is the accumulated sum of rewards during an episode. The agent interacts with the environment by taking actions and observing rewards, and learns which sequences of actions lead to the highest reward. SeaPearl is fully implemented in Julia language, avoiding the overhead of Python calls from a C++ solver and is available on Github. SeaPearl is a constraint programming solver that uses reinforcement learning to solve combinatorial optimization problems. The solver is minimalist, with a focus on extensibility and flexibility, and consists of a constraint programming solver and a reinforcement learning model. It uses graph neural networks to embed learning in constraint programming and can handle any CP model. The solving process involves a tripartite graph representation of the problem, which is fed into a graph neural network to compute a latent vector and make branching decisions. The solver is trained using randomly generated instances, and the learning is conducted on randomly selected instances from the training set. SeaPearl uses a deep architecture consisting of 4 graph attention layers and 5 fully-connected layers, which can be easily defined using the library Flux. The solver can be used to solve the graph coloring problem, where the goal is to assign labels to each node such that adjacent nodes have a different label. SeaPearl is a constraint programming solver that uses reinforcement learning to solve hard problems such as graph coloring and traveling salesman with time heuristics for value-selection. The training routines can be defined by the user, including the value-selection to be trained, the instance generator, the number of episodes, the search strategy, and the variable heuristic. Once trained, the heuristic can be used to solve new instances. SeaPearl allows the user to redefine types without requiring changes to the source code. The implementation illustrates only a small subset of the functionalities of the solver. Many other components, such as the reward or the state representation, can be redefined by the end-user for prototyping new research ideas.
The experiments evaluate the ability of SeaPearl to learn good heuristics for value-selection. Instances for training the models have been generated randomly with a custom generator. Training is done until convergence, limited to 13 hours on AWS' EC2 with 1 vCPU of Intel Xeon capped to 3.0GHz, and memory consumption is capped to 32 GB. The evaluation is done on other instances (still randomly generated in the same manner) on the same machine. Comparisons against greedy heuristics on two NP-hard problems are proposed.
SeaPearl is a Constraint Programming (CP) solver that uses Reinforcement Learning (RL) and a graph attention network to find optimal solutions for the Travelling Salesman Problem with Time Windows (TSPTW). Instances are generated using a generator from a previous study, and a graph representing the current TSPTW instance is used instead of the default tripartite graph. The CP model is based on a dynamic programming formulation, and the neural architecture is based on the same design choices as in the previous study. The goal is to minimize the sum of travel distances while respecting time windows. The performance profiles and training curves for the DQN agent are presented for instances with 20 and 30 nodes. Results show that the heuristic performances can be roughly equaled, and that the learned heuristic is able to reproduce the behavior of the min-value heuristic.
SeaPearl is a constraint programming solver that uses reinforcement learning to improve efficiency. The paper proposes future work on having two specialized agents, one to find good solutions and one to prove optimality, and finding ways to prioritize these tasks. The paper also suggests extending the learning to variable selection and selecting an appropriate neural network architecture. The tool aims to facilitate future research in the hybrid SeaPearl is a hybrid solver that combines constraint programming with deep reinforcement learning to solve combinatorial optimization problems. It reduces inference costs and enables accurate predictions. However, it faces challenges when dealing with real-world instances and problems with large action space. SeaPearl draws on probability distributions, optimization algorithms, and graph convolutional neural networks. It is open-source and built on top of the Gecode constraint programming library, with a focus on graph coloring and scheduling. The solver has shown promising results on several benchmark problems.
1754 word summary
SeaPearl is a constraint programming solver guided by reinforcement learning. It uses machine learning and graph theory to solve combinatorial optimization problems, with a focus on graph coloring and scheduling. SeaPearl is built on top of the Gecode constraint programming library and is available in Scala. The solver is guided by reinforcement learning, which allows it to learn from experience and improve its performance over time. SeaPearl has been tested on several benchmark problems and has shown promising results. SeaPearl is a constraint programming solver that incorporates reinforcement learning. The solver relies on several resources, including probability distributions, optimization algorithms, and graph convolutional neural networks. It also draws on open-source software for integer and constraint programming. In developing SeaPearl, researchers have cited a range of studies that explore reinforcement learning, pruning, value networks, and other topics related to computer science and optimization. SeaPearl is a constraint programming solver that uses reinforcement learning (RL) to guide heuristics. It combines techniques from constraint programming, artificial intelligence, and operations research. SeaPearl's approach is based on machine learning for combinatorial optimization using RL. The solver is open-source and can help the community in the development of new hybrid approaches for tackling optimization challenges. Many open challenges should be addressed for an efficient use of machine learning methods inside a solving process. SeaPearl is a hybridization of constraint programming and deep reinforcement learning that proposes a flexible, easy-to-use, and open-source research framework for solving combinatorial optimization problems. The combination of machine learning approaches with a search procedure enables accurate prediction and reduces heavy inference costs of deep models in low-resource settings. However, developing such hybrid approaches requires a combination of constraint programming and reinforcement learning, which is still a challenge as many issues must be tackled. One way to tackle real-world instances is to modify slightly the available instances by introducing small perturbations on them. Another difficulty is to deal with problems having a large action space, which makes the learning more difficult and reduces the generalization to large instances. A possible direction could be to reduce the size of the action space using a dichotomy selection. SeaPearl is a constraint programming solver that uses reinforcement learning to improve efficiency. The paper proposes future work on having two specialized agents, one to find good solutions and one to prove optimality, and finding ways to prioritize these tasks. The paper also suggests extending the learning to variable selection and selecting an appropriate neural network architecture. The tool aims to facilitate future research in the hybridization of constraint programming and deep reinforcement learning. The paper presents results showing that the learned heuristic outperforms the heuristic baseline with a factor of three in terms of the number of nodes visited. SeaPearl is a Constraint Programming (CP) solver that uses Reinforcement Learning (RL) and a graph attention network to find optimal solutions for the Travelling Salesman Problem with Time Windows (TSPTW). Instances are generated using a generator from a previous study, and a graph representing the current TSPTW instance is used instead of the default tripartite graph. The CP model is based on a dynamic programming formulation, and the neural architecture is based on the same design choices as in the previous study. The goal is to minimize the sum of travel distances while respecting time windows. The performance profiles and training curves for the DQN agent are presented for instances with 20 and 30 nodes. Results show that the heuristic performances can be roughly equaled, and that the learned heuristic is able to reproduce the behavior of the min-value heuristic. Comparisons are done with a problem using the smallest domain as variable ordering. The experiments are based on a standard CP formulation of the graph coloring problem. SeaPearl is a constraint programming solver that utilizes reinforcement learning to solve hard problems such as graph coloring and traveling salesman with time heuristics for value-selection. The goal of the experiments is to evaluate the ability of SeaPearl to learn good heuristics for value-selection. Instances for training the models have been generated randomly with a custom generator. Training is done until convergence, limited to 13 hours on AWS' EC2 with 1 vCPU of Intel Xeon capped to 3.0GHz, and memory consumption is capped to 32 GB. The evaluation is done on other instances (still randomly generated in the same manner) on the same machine. Comparisons against greedy heuristics on two NP-hard problems are proposed. The implementation, the models, and the results are released in open-source with the solver.
The training routines can be defined by the user, including the value-selection to be trained, the instance generator, the number of episodes, the search strategy, and the variable heuristic. Once trained, the heuristic can be used to solve new instances. The last snapshot shows how the training routines can be defined. SeaPearl allows the user to redefine types without requiring changes to the source code of SeaPearl. This has been made possible thanks to the multiple dispatching functionality of Julia and has made it easier for users to prototype new research ideas. The implementation illustrates only a small subset of the functionalities of the solver. Many other components, such as the reward or the state representation, can be redefined by the end-user for prototyping new research ideas. SeaPearl is a constraint programming solver that uses deep reinforcement learning to solve problems efficiently. It is built using the Julia programming language, which is efficient and rich in both mathematical programming and machine learning libraries. The solver can be used to solve the graph coloring problem, where the goal is to assign labels to each node such that adjacent nodes have a different label. SeaPearl uses a deep architecture consisting of 4 graph attention layers and 5 fully-connected layers, which can be easily defined using the library Flux. The solving process can then be run, where the objective and constraints are added to the model. SeaPearl also uses a neural network to obtain a score for each possible value and a fully-connected neural network to select the best solution. SeaPearl is a constraint programming solver that uses reinforcement learning. The solving process involves a tripartite graph representation of the problem, which is fed into a graph neural network to compute a latent vector and make branching decisions. The solver can handle any CP model and uses problem-dependent features. The state representation is designed to be generic and handle any triplet of inputs. The solver is trained using randomly generated instances. SeaPearl is a constraint programming solver that uses reinforcement learning to solve combinatorial optimization problems. The solver integrates a support training algorithm that returns a vector of weights used for parametrizing the neural network. The learning is conducted on randomly selected instances from the training set. At the beginning of each new episode, an instance of the problem is used to train the model. The reward signal consists of two terms, one dedicated to finding a feasible solution and another to finding the best feasible solution. The transition function updates the current state according to the action that has been selected, and the action corresponds to a value that can be assigned to a variable of the CP model. The state contains information about the instance that is solved, the current state of the solving process, and different representations are proposed in the case studies. SeaPearl is a constraint programming solver with reinforcement learning. It defines a state as a triplet of the number of backtracks, statistics of the solving process, and the associated CP model. The reinforcement learning environment is designed to represent the behavior of combinatorial problems. SeaPearl aims to improve the CP solving process using knowledge from previously solved problems. The solver is minimalist, with a focus on extensibility and flexibility. SeaPearl's architecture consists of a constraint programming solver and a reinforcement learning model. SeaPearl uses graph neural networks to embed learning in constraint programming. SeaPearl is a solver that uses constraint programming and reinforcement learning to solve combinatorial optimization problems on graphs. It learns a p-dimensional representation for each node in the graph using a Graph Neural Network (GNN) that aggregates information from neighboring nodes. Reinforcement learning algorithms are used to find a policy that maximizes the total return, which is the accumulated sum of rewards during an episode. The agent interacts with the environment by taking actions and observing rewards, and learns which sequences of actions lead to the highest reward. SeaPearl is a constraint programming solver that combines reinforcement learning (RL) and graph neural networks to train agents to take actions in an environment to maximize a cumulated reward. The RL component is fully integrated inside the solver, and the reinforcement learning environment allows CP backtracking inside an episode. SeaPearl is built upon the proof of concept proposed by Cappart et al. and proposes an architecture able to solve CP models, whereas previous solvers were restricted to dynamic programming models. SeaPearl is fully implemented in Julia language, avoiding the overhead of Python calls from a C++ solver. Experiments on two toy-problems, namely the graph coloring and the traveling salesman problem with time windows, are proposed. The code is available on Github. The philosophy behind SeaPearl is to ease and speed up the development process to any researcher desiring to design learning-based approaches to improve constraint programming solvers. SeaPearl is a new solver that combines constraint programming and reinforcement learning to efficiently solve combinatorial optimization problems. The goal of combinatorial optimization is to find an optimal solution among a finite set of possibilities. Various approaches have been developed to solve these problems, including SAT solvers and mixed-integer programming. However, these approaches have limitations, and the use of machine learning-based heuristics has shown promise in improving their performance.
SeaPearl aims to leverage historical data related to specific problems in order to solve future instances more quickly. The solver is implemented in JuMP, an open-source framework, and includes a learning component that uses reinforcement learning to guide the search process. SeaPearl also includes support for modeling the learning component and for learning branching decisions using machine learning routines.
The performance of SeaPearl was evaluated on two problems, and although it is not yet competitive with industrial solvers, it shows promise as a flexible and efficient solver. The hybridization of constraint programming and machine learning is challenging to build, but SeaPearl demonstrates that reinforcement learning can be successfully used to drive the search process. SeaPearl is a constraint programming solver that uses reinforcement learning. The team behind it includes Felix Chalumeau, Ilan Coulon, Quentin Cappart, and Louis-Martin.