Toward an adaptive deep reinforcement learning agent for maritime platform defense

Jared Markowitz; Edward W. Staley

doi:10.1117/12.2663831

12 June 2023 Toward an adaptive deep reinforcement learning agent for maritime platform defense

Jared Markowitz, Edward W. Staley

Proceedings Volume 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V; 125380O (2023) https://doi.org/10.1117/12.2663831
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

We explore strategies for improving the versatility of deep reinforcement learning (DRL) agents trained for maritime platform defense, in an effort to avoid impractical retraining when conditions change. DRL platform defense agents must be able to effectively schedule countermeasures, with constraints and against incoming raids of threats of uncertain type. Here we provide methods, centered on domain randomization, threat representation, and neural network architecture modification, for addressing changes in the relative locations and orientations of ships in the fleet, the number of ships and their inventories, and the distribution of threats. Testing our interventions in a realistic simulator, we show that a base DRL agent may be extended to account for a wide variety of changes in the operational scenario, with little degradation of performance.

Conference Presentation

Citation Download Citation

Jared Markowitz and Edward W. Staley "Toward an adaptive deep reinforcement learning agent for maritime platform defense", Proc. SPIE 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V, 125380O (12 June 2023); https://doi.org/10.1117/12.2663831

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