- piCamera + Lidiar Sensor + Thymio's Sensors
- Raspberry Pi 4
Q-learning is a machine learning approach that enables a model to iteratively learn and improve over time by taking the correct action. IT is also a type of reinforcement learning.
With reinforcement learning, a machine learning model is trained to mimic the way animals or children learn. Good actions are rewarded or reinforced, while bad actions are discouraged and penalized.
With the state-action-reward-state-action form of reinforcement learning, the training regimen follows a model to take the right actions. Q-learning provides a model-free approach to reinforcement learning. There is no model of the environment to guide the reinforcement learning process. The agent -- which is the AI component that acts in the environment -- iteratively learns and makes predictions about the environment on its own.
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username: pi
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Password : RoboRobo
- flatpak run --command=thymio-device-manager org.mobsya.ThymioSuite
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cd ~/BreezySLAM/python/breezyslam
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python3.11 SLAM.py
- cd visualizer
- python3.11 Visualizer.py
- run the server pi@Robo:
- open another terminal locally and run : scp <path_of_the_file> [email protected]:/home/pi/
- add password : RoboRobo
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run gnuplot
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load "simple_visualization.gnu"
cd distanceTestsLidiar
• cd image_processing : Image recognition, object detection, colour recognition.
cd simulation-deepLearning > q-learning-simulation.py
cd Controller > Robot_Controller.py