https://github.com/abdullah-al-masud/rl-optimize
Neural Combinatorial Optimization with Reinforcement Learning
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Combinatorial Optimization is a category of problems which requires optimizing a function over a combination of discrete objects and the solutions are constrained. Examples include finding shortest paths in a graph, maximizing value in the Knapsack problem and finding boolean settings that satisfy a set of constraints. Many of these problems are NP-Hard, which means that no polynomial time solution can be developed for them. Instead, we can only produce approximations in polynomial time that are guaranteed to be some factor worse than the true optimal solution.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Neural Combinatorial Optimization with Reinforcement Learning
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Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions
Rethinking Differentiable Search for Mixed-Precision Neural Networks
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출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.