Towards Generalizable RL-Based Task Offloading for Vehicular Edge Computing

Deep reinforcement learning (DRL) has been used very successfully for a variety of applications in the field of vehicular networking. We investigate the generalizability of a DRL-based scheduling approach for task offloading in vehicular edge computing (VEC). We propose a hybrid scheduling policy that combines heuristic task prioritization with a learned DQN-based resource selection. Two deep Q-learning (DQN) architectures are compared: a conventional multilayer perceptron (MLP) with fixed-size inputs; and a permutation-invariant architecture that uses a lightweight transformer encoder. Trained exclusively on a simple straight-road scenario, the permutation-invariant DQN achieves perfect selection of the fastest processing vehicle when tested in an unseen realistic scenario. In contrast, the MLP-based DQN suffers severe degradation due to positional bias and padding artifacts. Results demonstrate that set-based, permutation-invariant representations are essential for sample efficiency and generalization in VEC environments with high variability.