Reinforcement Learning Q&A Session

Pursuit-evasion games have been studied for decades under perfect information and analytically-derived policies for static environments. Here, we incorporate uncertainty in a target’s position via simulated measurements and propose a continuous deep reinforcement learning (RL) approach to support pursuit of a speed-advantaged target. An OpenAI gym environment was created for simulating pursuit game play. A Kalman filter was implemented for simulating data fusion of uncertainties associated with imperfect range and bearing measurements from the pursuer to the target. An actor-critic based, model-free, deep discrete policy gradient (DDPG) method was implemented for incrementally training an agent to compete at a speed-overmatched pursuit game. The resulting RL policy was tested under many scenarios and performance exceeded that of a baseline course-aligned strategy. Emerging reinforcement learning success strategies were analyzed and manually abstracted into grouped behaviors. An online decision-making framework is discussed for leveraging conditional past performance to predict future performance.

Reinforcement Learning Approach to Speed-Overmatched Maritime Targets with Uncertain Information

**Moderator:** John Emanuello

**Speakers:**

[Ahmad Ridley](https://www.underline.io/speakers/77663-ahmad-ridley)

[Elizabeth Gilmour](https://www.underline.io/speakers/77545-elizabeth-gilmour)
     
[Julia Allen](https://www.underline.io/speakers/77564-julia-allen)

[Christianne Izumigawa](https://www.underline.io/speakers/78912-christianne-izumigawa)
        
Roger A. Hallman


Reinforcement Learning

live discussion

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

**Watch this video to hear about the AFCEA purpose.**

<iframe width=1280 height=706 frameborder="0" scrolling="no" src="https://screencast-o-matic.com/player/creleVVcvRh?v=6&ff=1&title=0&controls=1" allowfullscreen="true"></iframe>

Julia Allen

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Presentations

Reinforcement Learning Q&A Session

Reinforcement Learning Approach to Speed-Overmatched Maritime Targets with Uncertain Information

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