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Loic Guegan 2022-11-01 17:30:25 +01:00
parent 69c5d709a4
commit 451856be1b
1 changed files with 24 additions and 3 deletions
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27
qlearning.py
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@ -38,7 +38,12 @@ def isWall(h,game):
return(True)
return(False)
last_state=None
last_action=None
def event_handler(game,event):
global last_state,last_action
h=game.snake[0]
left=(h[0]-1,h[1])
right=(h[0]+1,h[1])
@ -70,14 +75,30 @@ def event_handler(game,event):
# This come from me I do not now if it is the best way to identify a state
state=2**11*snake_go_up+2**10*snake_go_right+2**9*snake_go_down+2**8*snake_go_left+2**7*apple_up+2**6*apple_right+2**5*apple_down+2**4*apple_left+2**3*obstacle_up+2**2*obstacle_right+2**1*obstacle_down+obstacle_left
# Choose an action
if np.max(qtable[state]) > 0:
action = np.argmax(qtable[state])
else:
action=random.choice((12,3,6,9))
action=random.choice((0,1,2,3))
game.direction=action
# Update current state Q
if last_state != None:
qtable[last_state,last_action]=qtable[last_state,last_action]+0.5*(reward+0.5*qtable[state])
last_state=state
last_action=action
# Apply the action
snake_action=12
if action==1:
snake_action=3
elif action==2:
snake_action=6
elif action==3:
snake_action=9
game.direction=snake_action
for i in range(0,10):
last_state=None
last_action=None
score=game.run(event_handler=event_handler)
print("Game ended with "+str(score))