MPFlow: Learning Budgeted Max-Flow Optimization on the Lightning Network with Deep Graph Reinforcement Learning

TANGCO: Learning Topology-Aware Capacity Allocation for Overload-driven Cascading Failures

This work introduces TANGCO (Topology-Aware Neural Graph-Guided Capacity Optimization), which uses a graph neural network policy trained through the cascade simulator with policy-gradient learning and a heuristic anchor to allocate a fixed capacity budget across nodes to resist cascades under local load redistribution.

Learning to Optimize: Joint Routing and Flow Allocation on Sparse Non-Euclidean Networks

This work proposes Double-Channel Graph Attention (DCGA), an end-to-end reinforcement learning framework that isolates network reachability and demand-service logic into separate graph channels and constructs valid routes using a simulator-coupled, constraint-informed decoder.

Application of Reinforcement Learning for Optimizing the Capacitated Vehicle Routing Problem

This study proposes a reinforcement learning (RL) framework for solving the deterministic single-depot Capacitated Vehicle Routing Problem (CVRP). The Capacitated Vehicle Routing Problem is formulated as a Markov Decision Process and a REINFORCE agent with a linear-softmax policy, incorporating Clarke-Wright savings features, is trained as a proof-of-concept prior to future deep architectures such as Deep Q-Network and Proximal Policy Optimization. The proposed agent is trained and evaluated on three reproducible synthetic benchmark datasets comprising 20, 50, and 100 customers, and its performance is compared with two conventional construction methods, namely Nearest Neighbor and Clarke-Wright Savings. The results show that the learned policy consistently converges to a stable routing strategy and outperforms the Nearest Neighbor heuristic on the medium- and large-scale instances, reducing total travel distance by 8.3% and 10.0% respectively, while remaining within 13.8-20.8% of the Clarke-Wright benchmark across all scenarios. Inference is completed within milliseconds once training is finished, indicating that the learned policy can be reused across new routing instances without restarting the optimization process. These findings demonstrate that reinforcement learning is a promising and scalable alternative to conventional heuristic and metaheuristic approaches for capacitated routing problems, particularly in dynamic logistics environments that require rapid and adaptive decision making.

GPlaceRL: An Open-Source Graph Reinforcement Learning Framework for Detailed Placement

Bidirectional job-shop scheduling with graph-theoretic features: a graph attention network and proximal policy optimization approach

A novel DRL-based approach that integrates bidirectional scheduling with graph-theoretic features to effectively solve JSSP is introduced, which achieves superior performance compared to traditional heuristics and classic DRL methods, while maintaining competitive results against recent state-of-the-art approaches.

GraphRoute-Transfer: Topology-Generalizable Routing Convergence Optimization via Graph Reinforcement Learning

GraphRoute-Transfer, a graph-neural-network policy that assigns per-node timers from local structural features and is by construction permutation- and size-invariant, is proposed, a graph-neural-network policy that assigns per-node timers from local structural features and is by construction permutation- and size-invariant.