A Deep Reinforcement Learning Framework for Dynamic Routing of Distant-Water Squid-Jigging Vessels: PPO-Based Retrospective Simulation in the Peruvian Jumbo Flying Squid Fishery

Distant-water squid-jigging operations require continuous route decisions that balance expected fishing returns and fuel consumption under spatially heterogeneous fishery resources and oceanographic conditions. This study proposes a Proximal Policy Optimization (PPO)-based dynamic routing framework for the Peruvian jumbo flying squid fishery. A data-informed gridded simulator integrates oceanographic variables, historical fishing-ground information, a wave-induced fuel adjustment, and an MVT-Inspired Local Depletion Mechanism. The routing task is formulated as an augmented-state Markov decision process with a multi-component reward function considering economic return, historical fishing-ground guidance, wave-related operating effects, and operational inefficiency. Vessel-level cross-fitting is used to reduce data reuse between reward-environment construction and historical-prior construction, and the policy is evaluated retrospectively in a held-out 2021 simulation environment. Across three independent training seeds, PPO achieved 257.42 ± 13.92 t of Cumulative Simulated Catch, 256.59 ± 7.83 t of Cumulative Fuel Consumption, and a Simplified Simulated Operating Margin of 129,376.80 ± 24,244.68 USD. Relative to the prespecified representative Historical Trajectory Replay, these results represent 13.77% higher simulated catch, 8.58% lower simulated fuel consumption, and 85.88% higher simulated operating margin. Additional sensitivity analyses showed that the qualitative catch–fuel–margin advantage was retained under moderate perturbations of the held-out catch field and across ±20% squid- and fuel-price variations. The results indicate that PPO provides a more favorable and comparatively stable catch–fuel–margin trade-off within the constructed retrospective simulation framework.