aiqbench analysis
This article details a hybrid neural architecture combining Q-learning with Pointer Networks to tackle combinatorial optimization problems like the Travelling Salesman Problem, though it is now withdrawn due to identified limitations in experimental rigor. The study represents an attempt to improve long-term outcome optimality through dynamic attention adjustments based on state-action values within a Markov Decision Process framework.
Pointer Networks with Q-Learning for Combinatorial Optimization
arXiv:2311.02629v5 Announce Type: replace Abstract: We introduce the Pointer Q-Network (PQN), a hybrid neural architecture that integrates model-free Q-value policy approximation with Pointer Networks (Ptr-Nets) to enhance the optimality of attention-based sequence generation, focusing on long-term outcomes. This integration proves particularly effective in solving combinatorial optimization (CO) tasks, especially the Travelling Salesman Problem (TSP), which is the focus of our study. We address this challenge by defining a Markov Decision Process (MDP) compatible with PQN, which involves iterative graph embedding, encoding and decoding by an LSTM-based recurrent neural network. This process generates a context vector and computes raw attention scores, which are dynamically adjusted by Q-values calculated for all available state-action pairs before applying softmax. The resulting attention vector is utilized as an action distribution, with actions selected hinged to exploration-exploitation dynamic adaptibility of PQN. Our empirical results demonstrate the efficacy of this approach, also testing the model in unstable environments.
Abstract:We introduce the Pointer Q-Network (PQN), a hybrid neural architecture that integrates model-free Q-value policy approximation with Pointer Networks (Ptr-Nets) to enhance the optimality of attention-based sequence generation, focusing on long-term outcomes. This integration proves particularly effective in solving combinatorial optimization (CO) tasks, especially the Travelling Salesman Problem (TSP), which is the focus of our study. We address this challenge by defining a Markov Decision Process (MDP) compatible with PQN, which involves iterative graph embedding, encoding and decoding by an LSTM-based recurrent neural network. This process generates a context vector and computes raw attention scores, which are dynamically adjusted by Q-values calculated for all available state-action pairs before applying softmax. The resulting attention vector is utilized as an action distribution, with actions selected hinged to exploration-exploitation dynamic adaptibility of PQN. Our empirical results demonstrate the efficacy of this approach, also testing the model in unstable environments.
The author has identified limitations in the experimental methodology and evaluation, as well as conclusions that are not sufficiently supported by the available evidence. The manuscript is therefore being withdrawn, as it no longer meets the author's standards for scientific rigor
Machine Learning (cs.LG); Optimization and Control (math.OC)
arXiv:2311.02629 [cs.LG]
(or arXiv:2311.02629v5 [cs.LG] for this version)
Full text under the publisher's open licence · 202 words
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