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This project, uses Q-Learning, to allow an agent to find the best path to collect packages in 3 separate scenarios, 1 , 3 and specific order collection. This is simulated in a FourRooms environment which can be deterministic or stochastic
MatthewWeppenaar/FourRooms-QLearning
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To create a virtual environment: cd to working directory in terminal Enter "make" in terminal IMPORTANT NOTE: This make file will only work when running on Ubuntu If you want to run this on Mac change: "test -d venv || virtualenv -p python3 venv" To: "test -d venv || python3 -m venv venv" To run you must activate your virtual environment in working directory: source ./venv/bin/activate 2 example runs are included for each Scenario: In working directory(after environment has been created) enter make run: runs Scenario1.py with no flag and saves an image of the best/last run. make run2: runs Scenario1.py with the '-stocastic' flag and saves an image of the best/last run. make run3: runs Scenario2.py with no flag and saves an image of the best/last run. make run4: runs Scenario2.py with the '-stocastic' flag and saves an image of the best/last run. make run5: runs Scenario3.py with no flag and saves an image of the best/last run. make run6: runs Scenario3.py with the '-stocastic' flag and saves an image of the best/last run. Make clean: removes virtual environment Scenario 1-takes about 0.5 seconds to run on my PC whereas it takes about 2 seconds to run on nightmare Scenario 2-takes about 123 seconds to run on my PC whereas it takes about 303 seconds to run on nightmare Scenario 3-takes about 0.3 seconds to run on my PC whereas it takes about 3 seconds to run on nightmare
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This project, uses Q-Learning, to allow an agent to find the best path to collect packages in 3 separate scenarios, 1 , 3 and specific order collection. This is simulated in a FourRooms environment which can be deterministic or stochastic
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