Control systems for unmanned undersea vehicles (UUVs) are typically implemented using proportional integral derivative (PID) control systems. PID control systems for UUVs are resource-intensive to tune because they require several engineers, marine operators, and ship crew working together to adjust the controller. Furthermore, PID controllers rely on complex dynamic system models that contain assumptions to reduce the computational complexity of the models but can degrade the controller's performance if an environmental condition is encountered that conflicts with an assumption. In this study, a deep reinforcement learning (DRL) control system based on the Deep Deterministic Policy Gradient (DDPG) algorithm is investigated for a UUV control system. The DDPG algorithm is model-free, meaning that a complex dynamic system model is not needed to learn and provide optimal control performance. DRL-based control systems are tuned autonomously, which can greatly reduce the resources needed for tuning. The drawback to deep reinforcement learning is that it is computationally and resource-intensive. This study investigates how mixed floating-point precision with loss scaling can be used to reduce the time and computational resources needed to train the DDPG agent. Numerical case studies are presented.

Mixed Precision Reinforcement Learning for Control Simulation of Unmanned Undersea Vehicles


**Title:** Reinforcement Learning

**Moderator:**  Christopher Rosser, Naval Information Warfare Center Pacific


               
        


  Reinforcement Learning - Q&A

technical paper

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Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

To spread awareness of current machine learning research relevant to Naval applications

To connect machine learning researchers with experts in military requirements

To build and strengthen collaborations within the defense research community


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NAML 2022

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop. The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

To spread awareness of current machine learning research relevant to Naval applications

To connect machine learning researchers with experts in military requirements

To build and strengthen collaborations within the defense research community



Eugene Chabot

1

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Mixed Precision Reinforcement Learning for Control Simulation of Unmanned Undersea Vehicles

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