This is a research survey paper. The topic I will be tackling with is using deep reinforcement leanring to solve combinatorial optimization problem. I will be including all the research paper that I will be using for reference here.
Sections
Using approaches other than neural network
- WORST-CASE ANALYSIS OF A NEW HEURISTIC FOR THE TRAVELLING SALESMAN PROBLEM: https://apps.dtic.mil/dtic/tr/fulltext/u2/a025602.pdf
- Concorde TSP Solver: http://www.math.uwaterloo.ca/tsp/concorde/
- Implementing the Dantzig-Fulkerson-Johnson algorithm for large traveling salesman problems: https://www.math.uwaterloo.ca/~bico/papers/dfj_mathprog.pdf
- An effective heuristic algorithm for the traveling-salesman problem: https://www.cs.princeton.edu/~bwk/btl.mirror/tsp.pdf
- Google Or-Tools: https://developers.google.com/optimization/routing
- Critical analysis of hopfield's neural network model for TSP and its comparison with heuristic algorithm for shortest path computation: https://github.com/ThapaRahul/survey-on-deep-learning-for-combinatorial-optimization/blob/master/sarwar.pdf
- Comparison of Neural Networks for Solving the Travelling Salesman Problem: https://github.com/ThapaRahul/survey-on-deep-learning-for-combinatorial-optimization/blob/master/maire.pdf
Using neural networks
Hopfield
- Neural networks and physical systems with emergent collective computational abilities: https://www.pnas.org/content/pnas/79/8/2554.full.pdf
- Neurons with graded response have collective computational properties like those of two-state neurons: https://www.pnas.org/content/pnas/81/10/3088.full.pdf
- "Neural" Computation of Decisions in Optimization Problems: https://github.com/ThapaRahul/survey-on-deep-learning-for-combinatorial-optimization/blob/master/hopfield-1985.pdf
- A theoretical investigation into the performance of the Hopfield model: https://github.com/ThapaRahul/survey-on-deep-learning-for-combinatorial-optimization/blob/master/aiyer-hopfield.pdf
Elastic Net
- An analogue approach to the travelling salesman problem using an elastic net method: https://www.nature.com/articles/326689a0.pdf?origin=ppub
Self Organizing Maps
- Self-organizing feature maps and the Travelling Salesman Problem.
- Solving a combinatorial problem via self-organizing process: an application of the Kohonen algorithm to the traveling salesman problem
- The self-organizing map: https://sci2s.ugr.es/keel/pdf/algorithm/articulo/1990-Kohonen-PIEEE.pdf
- Critical analysis of hopfield's neural network model for TSP and its comparison with heuristic algorithm for shortest path computation: https://github.com/ThapaRahul/survey-on-deep-reinforcement-learning-for-combinatorial-optimization/blob/master/heuristic-algorithm.pdf
- Neural Networks for Combinatorial Optimization: A Review of More Than a Decade of Research: https://github.com/ThapaRahul/survey-on-deep-reinforcement-learning-for-combinatorial-optimization/blob/master/Neural_Networks_for_Combinatorial_Optimization_A_R.pdf
Using Deep Learning
- Pointer Networks: https://github.com/ThapaRahul/survey-on-deep-reinforcement-learning-for-combinatorial-optimization/blob/master/pointer-networks.pdf
- Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search: http://papers.nips.cc/paper/7335-combinatorial-optimization-with-graph-convolutional-networks-and-guided-tree-search.pdf
- Neural Combinatorial Optimization with Reinforcement Learning: https://github.com/ThapaRahul/survey-on-deep-reinforcement-learning-for-combinatorial-optimization/blob/master/Week11-.pdf
- Learning Combinatorial Optimization Algorithms over Graphs: https://github.com/ThapaRahul/survey-on-deep-reinforcement-learning-for-combinatorial-optimization/blob/master/rl-graph.pdf
- Exploratory Combinatorial Optimization with RL: https://arxiv.org/pdf/1909.04063.pdf
- Reinforcement Learning for Solving the Vehicle Routing Problem: http://papers.nips.cc/paper/8190-reinforcement-learning-for-solving-the-vehicle-routing-problem.pdf
- Solving a New 3D Bin Packing Problem with Deep Reinforcement Learning Method: https://arxiv.org/pdf/1708.05930.pdf
- Solving Combinatorial Optimization Tasks by Reinforcement Learning: A General Methodology Applied to Resource-Constrained Scheduling: http://www.eecs.harvard.edu/~parkes/cs286r/spring06/papers/zhangdietterich_jair00.pdf
- Ant-Q: A Reinforcement Learning approach to the traveling salesman problem: http://people.idsia.ch/~luca/ml95.pdf
- Deep RL: An Overview: https://arxiv.org/pdf/1701.07274.pdf?source=post_page
- Learning Heuristics over Large Graphs via Deep RL: https://arxiv.org/pdf/1903.03332.pdf
- Machine Learning for Combinatorial Optimization: a Methodological Tour d’Horizon: https://arxiv.org/pdf/1811.06128.pdf
- An Introduction to Deep Reinforcement Learning: https://arxiv.org/pdf/1811.12560.pdf
- Neural computation of decisions in optimisation problems: https://github.com/ThapaRahul/survey-on-deep-reinforcement-learning-for-combinatorial-optimization/blob/master/tsp.pdf
- Comparison of Neural Networks for Solving the Travelling Salesman Problem: https://github.com/ThapaRahul/survey-on-deep-reinforcement-learning-for-combinatorial-optimization/blob/master/comparison-tsp.pdf
- Learning to Learn for Global Optimization of Black Box Functions: https://arxiv.org/pdf/1611.03824v1.pdf
- No Free Lunch Theorems for Optimization: https://github.com/ThapaRahul/survey-on-deep-reinforcement-learning-for-combinatorial-optimization/blob/master/no-free-lunch.pdf
- Neural architecture search with reinforcement learning: https://arxiv.org/pdf/1611.01578.pdf
- Global search in combinatorial optimization using reinforcement learning algorithms: https://github.com/ThapaRahul/survey-on-deep-reinforcement-learning-for-combinatorial-optimization/blob/master/Global_search_in_combinatorial_optimization_using_.pdf
- A Brief Survey of Deep Reinforcement Learning: https://arxiv.org/pdf/1708.05866.pdf
- CONTINUOUS CONTROL WITH DEEP REINFORCEMENT LEARNING: https://arxiv.org/pdf/1509.02971.pdf?source=post_page
- Playing Atari with Deep Reinforcement Learning: https://arxiv.org/pdf/1312.5602.pdf?source=post_page