forked from jangirrishabh/toyCarIRL
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playing.py
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playing.py
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"""
Once a model is learned, use this to play it. that is run/exploit a policy to get the feature expectations of the policy
"""
from flat_game import carmunk
import numpy as np
from nn import neural_net
import sys
import time
NUM_STATES = 8
GAMMA = 0.9
def play(model, weights):
car_distance = 0
game_state = carmunk.GameState(weights)
_, state, __ = game_state.frame_step((2))
featureExpectations = np.zeros(len(weights))
# Move. dib book
#time.sleep(15)
while True:
time.sleep(0.01)
car_distance += 1
# Choose action.
action = (np.argmax(model.predict(state, batch_size=1)))
#print ("Action ", action)
# Take action.
immediateReward , state, readings = game_state.frame_step(action)
#print ("immeditate reward:: ", immediateReward)
#print ("readings :: ", readings)
#start recording feature expectations only after 100 frames
if car_distance > 100:
featureExpectations += (GAMMA**(car_distance-101))*np.array(readings)
#print ("Feature Expectations :: ", featureExpectations)
# Tell us something.
if car_distance % 2000 == 0:
print("Current distance: %d frames." % car_distance)
break
return featureExpectations
if __name__ == "__main__": # ignore
BEHAVIOR = sys.argv[1]
ITERATION = sys.argv[2]
FRAME = sys.argv[3]
saved_model = 'saved-models_'+BEHAVIOR+'/evaluatedPolicies/'+str(ITERATION)+'-164-150-100-50000-'+str(FRAME)+'.h5'
weights = [-0.79380502 , 0.00704546 , 0.50866139 , 0.29466834, -0.07636144 , 0.09153848 ,-0.02632325 ,-0.09672041]
model = neural_net(NUM_STATES, [164, 150], saved_model)
print (play(model, weights))