This is a quick programming game exercise on Markov Decision Processes and Dynamic Programming.

dependencies:

• numpy (>=1.10.x)
• gym (>=0.8.0)
• pygame (>=1.9.3)

type in terminal

sudo pip install gym numpy pygame

To install to your system:

git clone https://github.com/kiddos/guidehim.git
cd guidehim
sudo python setup.py install

The goal is to guide Stone Lin moving towards the goal. If your command move Stone Lin out of the Screen, game is over and you will need to reset the environemnt to play.

You might see these objects on the map:

• Empty: (grid value: 0)

• Wall: Stone Lin cannot run through wall (grid value: 1)

• Fire: If Stone Lin caught on fire, game over. (grid value: 2)

• Pond: If Stone Lin fell into a pond, also game over. (grid value: 3)

• Poop: It's ok to step on poop. Totally ok. (grid value: 4)

• 尻尻: It's not ok to 尻尻 in public. (grid value: 5)

• 肛肛: It's also not ok to 肛肛 someone else. (grid value 6)

• Stone Lin: G8 guy. (grid value: 8)

• goal: Stone Lin's goal. (grid value: 9)

Stone Lin does not always follow the command you gave him, since he is such a dick. He has a probablility of 87.87% to follow your command. For 12.13% chance, he will go the other way. This makes the game a little harder to play.

Your job is to gave Stone Lin action so that he can get to the goal.

import

from guidehim.guide_him import GuideHim

initialize environemnt:

env = GuideHim('map/simple.map')

reset the environemnt:

observation = env.reset()

give action:

observation, reward, done, _ = env.step(env.action_space.sample())

render:

env.render()

sample code:

from guidehim.guide_him import GuideHim
import numpy as np

def main():
  env = GuideHim('map/simple.map')
  observation = env.reset()
  while True:
    observation, reward, done, _ = env.step(env.action_space.sample())
    env.render()

if __name__ == '__main__':
  main()

I currently create 3 maps:

• empty.map
• simple.map
• easy.map

You can play around with these.

I implement 2 dynamic programming methods:

• value iteration value_iteration.py
• policy iteration policy_iteration.py

I suggest you implement your own first.