def main():
number_of_turns = 0 #going to use this for counting the number of steps before game end
catchCount = 0 #count of game ends
env = Maze(FILE_NAME)
myCat = Brain('Cat', env.cat.pos, env.actions)
myMouse = Brain('Mouse', env.mouse.pos, env.actions)
cheesePos = env.cheese.pos
board = env.mazeList
## DEBUGING
debug = False #Step by step toggle
env.renderWindow = False #start with graphics being rendered
while True:
if debug:
print('\nCLICK to start loop.')
env.win.getMouse()
print('==At start of loop, cat and mouse information:==')
myCat.printInfo()
myMouse.printInfo()
if debug:
print('\nCLICK to let mouse choose action.')
env.win.getMouse()
# print('Calling mouse.chooseRandom with catpos mousepos cheese pos:', myCat.pos, myMouse.pos, cheesePos)
mouseAction = myMouse.chooseAction(board, myCat.pos, myMouse.pos,
cheesePos)
mouseImmediateReward = env.moveMouse(mouseAction)
if debug:
print('immediate reward:', mouseImmediateReward)
print('myMouse.q_table:', myMouse.q_table)
print('\nCLICK to let cat choose action.')
env.win.getMouse()
# print('Calling cat.chooseRandom with catpos mousepos cheese pos:', myCat.pos, myMouse.pos, cheesePos)
catAction = myCat.chooseAction(board, myCat.pos, myMouse.pos,
cheesePos)
catImmediateReward = env.moveCat(catAction)
if debug:
print('catAction:', catAction)
print('immediate reward:', catImmediateReward)
print('myCat.q_table:', myCat.q_table)
print('\nCLICK to get feedback from environment.')
env.win.getMouse()
#get feedback from the environment
catPos, catReward, mousePos, mouseReward, done = env.turnEnd()
#add goal rewards if any
catImmediateReward += catReward
mouseImmediateReward += mouseReward
if debug:
print('catPos:', catPos, 'catImmediateReward:', catImmediateReward,
'mousePos:', mousePos, 'mouseImmediateReward:',
mouseImmediateReward, 'done:', done)
print('catReward:', catReward, 'mouseReward:', mouseReward)
print('\nCLICK to update agent Brain with positions.')
env.win.getMouse()
# Update agent's brains to reflect board positions after move
myMouse.updateBrain(catPos, catReward, mousePos, mouseReward)
myCat.updateBrain(catPos, catReward, mousePos, mouseReward)
myCat.printInfo()
myMouse.printInfo()
if debug:
print('\nCLICK to start learnLast step for both agents.')
env.win.getMouse()
#immediate learning of step taken
myMouse.learnLast(mouseImmediateReward)
myCat.learnLast(catImmediateReward)
myCat.printInfo()
myMouse.printInfo()
if debug:
print('\nCLICK to continue.')
#if something got caught, execute learning of agents
if done:
# time.sleep(1)
catchCount += 1
print('Hit something')
if debug:
print('mouse q-table before learnAll')
print(myMouse.q_table)
print('mouse history before learnAll')
print(myMouse.history)
myMouse.learnAll(mouseReward)
myCat.learnAll(catReward)
myCat.pos, myMouse.pos, cheesePos = env.restart(
) #using restart() so I can program in random spot spawning
# env.win.getMouse()
number_of_turns += 1
# if number_of_turns == 100:
# break
if catchCount % 1000 == 0:
env.renderWindow = True
if catchCount % 1001 == 2:
env.renderWindow = False
if (catchCount % 100 == 0):
saveAgent(myCat, catchCount)
saveAgent(myMouse, catchCount)
if catchCount == 1:
break
def testLoading(itNumber):
print('INIT base game..')
time.sleep(1)
catchCount = itNumber
env = Maze(FILE_NAME)
myCat = Brain('Cat', env.cat.pos, env.actions)
myMouse = Brain('Mouse', env.mouse.pos, env.actions)
cheesePos = env.cheese.pos
board = env.mazeList
print('loading from file')
loadAgent(myCat, catchCount)
loadAgent(myMouse, catchCount)
time.sleep(1)
print('showing agent info/q_tables')
myCat.printInfo()
myMouse.printInfo()
time.sleep(1)
print('testing running of agents from this point..')
time.sleep(1)
## DEBUGING
debug = True #Step by step toggle
env.renderWindow = True #start with graphics being rendered
while True:
if debug:
print('\nCLICK to start loop.')
env.win.getMouse()
print('==At start of loop, cat and mouse information:==')
myCat.printInfo()
myMouse.printInfo()
if debug:
print('\nCLICK to let mouse choose action.')
env.win.getMouse()
# print('Calling mouse.chooseRandom with catpos mousepos cheese pos:', myCat.pos, myMouse.pos, cheesePos)
mouseAction = myMouse.chooseAction(board, myCat.pos, myMouse.pos,
cheesePos)
mouseImmediateReward = env.moveMouse(mouseAction)
if debug:
print('immediate reward:', mouseImmediateReward)
print('myMouse.q_table:', myMouse.q_table)
print('\nCLICK to let cat choose action.')
env.win.getMouse()
# print('Calling cat.chooseRandom with catpos mousepos cheese pos:', myCat.pos, myMouse.pos, cheesePos)
catAction = myCat.chooseAction(board, myCat.pos, myMouse.pos,
cheesePos)
catImmediateReward = env.moveCat(catAction)
if debug:
print('catAction:', catAction)
print('immediate reward:', catImmediateReward)
print('myCat.q_table:', myCat.q_table)
print('\nCLICK to get feedback from environment.')
env.win.getMouse()
#get feedback from the environment
catPos, catReward, mousePos, mouseReward, done = env.turnEnd()
#add goal rewards if any
catImmediateReward += catReward
mouseImmediateReward += mouseReward
if debug:
print('catPos:', catPos, 'catImmediateReward:', catImmediateReward,
'mousePos:', mousePos, 'mouseImmediateReward:',
mouseImmediateReward, 'done:', done)
print('catReward:', catReward, 'mouseReward:', mouseReward)
print('\nCLICK to update agent Brain with positions.')
env.win.getMouse()
# Update agent's brains to reflect board positions after move
myMouse.updateBrain(catPos, catReward, mousePos, mouseReward)
myCat.updateBrain(catPos, catReward, mousePos, mouseReward)
myCat.printInfo()
myMouse.printInfo()
if debug:
print('\nCLICK to start learnLast step for both agents.')
env.win.getMouse()
#immediate learning of step taken
myMouse.learnLast(mouseImmediateReward)
myCat.learnLast(catImmediateReward)
myCat.printInfo()
myMouse.printInfo()
if debug:
print('\nCLICK to continue.')
#if something got caught, execute learning of agents
if done:
catchCount += 1
print('Hit something')
if debug:
print('mouse q-table before learnAll')
print(myMouse.q_table)
print('mouse history before learnAll')
print(myMouse.history)
myMouse.learnAll(mouseReward)
myCat.learnAll(catReward)
myCat.pos, myMouse.pos, cheesePos = env.restart(
) #using restart() so I can program in random spot spawning
if catchCount == 500000:
break
