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The bodyguard problem solved through classical Q-learning and off-policy particle swarms

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README.md

README.md

 
 

agent.py

agent.py

 
 

config.py

config.py

 
 

guard_q_table.dat

guard_q_table.dat

 
 

hostile_q_table.dat

hostile_q_table.dat

 
 

q_learner.py

q_learner.py

 
 

research-poster.png

research-poster.png

 
 

research-poster.svg

research-poster.svg

 
 

stats.txt

stats.txt

 
 

utils.py

utils.py

 
 

world.py

world.py

 
 

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Robotic Bodyguard Training Using Classical Q-learning and Off-policy Particle Swarms

This was my undergraduate research project under the UCF L.E.A.R.N. 2018 - 2019 apprenticeship.

Poster

alt text

Prerequisites

Make sure you have the following installed:

  • PyQt
  • PyQtGraph
  • PyGame

Running

Run the world.py file to start the simulation. Edit config.py to change various parameters like grid size and ghost counts.

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The bodyguard problem solved through classical Q-learning and off-policy particle swarms

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