The source code and dockerfile for the GSW2024 AI Lab.
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import minigrid
import gymnasium as gym
import random
import matplotlib.pyplot as plt
from minigrid.wrappers import ImgObsWrapper, RGBImgPartialObsWrapper
def main():
#samples = random.choices([0.0, 1.0], weights=(0.25, 0.75), k=100_000_000)
# print(samples)
# print(sum(samples))
#print(sum(samples) / len(samples))
names = [
"MiniGrid-Adv-8x8-v0",
"MiniGrid-AdvSimple-8x8-v0",
"MiniGrid-AdvSlippery-8x8-v0",
"MiniGrid-AdvLava-8x8-v0",
# "MiniGrid-SingleDoor-7x6-v0",
# "MiniGrid-DoubleDoor-10x8-v0",
# "MiniGrid-DoubleDoor-12x12-v0",
# "MiniGrid-DoubleDoor-16x16-v0",
# "MiniGrid-LavaSlipperyS12-v0",
# "MiniGrid-LavaSlipperyS12-v1",
# "MiniGrid-LavaSlipperyS12-v2",
# "MiniGrid-LavaSlipperyS12-v3",
# "MiniGrid-LavaCrossingS9N3-v0",
# "MiniGrid-SimpleCrossingS9N3-v0",
# "MiniGrid-ObstructedMaze-1Dlh-v0",
# "MiniGrid-BlockedUnlockPickup-v0",
# "MiniGrid-KeyCorridorS6R3-v0",
# "MiniGrid-LockedRoom-v0",
# "MiniGrid-KeyCorridorS3R1-v0",
# "MiniGrid-LavaGapS7-v0",
# "MiniGrid-DoorKey-8x8-v0",
# "MiniGrid-Dynamic-Obstacles-8x8-v0",
# "MiniGrid-Empty-Random-6x6-v0",
# "MiniGrid-Fetch-6x6-N2-v0",
# "MiniGrid-FourRooms-v0",
# "MiniGrid-LavaGapS7-v0",
# "MiniGrid-RedBlueDoors-6x6-v0",
]
for name in names:
env = gym.make(name)
env = RGBImgPartialObsWrapper(env)
env = ImgObsWrapper(env)
env.reset()
img = env.get_frame(highlight=False)
plt.title(name)
plt.imshow(img)
f = open(F"{name}.txt", "w")
f.write(env.printGrid(init=True))
f.close()
plt.show()
if __name__ == '__main__':
main()