def evaluator(rlAlgorithm,featureExtractor,nameAlg,nameFeat,multiple = 1.0,numTrials=50, epsilon = 0.3, sigma = 500):
ab = AngryBirdsMDP()
trial = 0
levelsPassed=[]
totalRewards =[]
name = nameAlg+'_'+nameFeat+'_'+str(64*multiple)
agent = angryAgent()
rl = rlAlgorithm(actions=lambda x: agent.getAngryBirdsActions(x,multiple),featureExtractor=featureExtractor, epsilon = epsilon)
if nameAlg == 'Q':
while trial<numTrials:
print(trial)
rl.explorationProb = epsilon
simulate(ab,rl,numTrials=1, maxIterations=1000, verbose=False, show=False)
rl.explorationProb = 0.0
outcome = simulate(ab,rl,numTrials=1, maxIterations=1000, verbose=False, show=False)
levelsPassed = levelsPassed + outcome['levelsPassed']
totalRewards = totalRewards + outcome['totalRewards']
print(totalRewards)
trial +=1
elif nameAlg == 'LSVI':
while trial<numTrials:
try:
print (trial)
rl.makeLSVI(epsilon)
simulate(ab,rl,numTrials=1, maxIterations=1000, verbose=False, show=False)
rl.makeLSVI(0.0)
outcome = simulate(ab,rl,numTrials=1, maxIterations=1000, verbose=False, show=False)
levelsPassed = levelsPassed + outcome['levelsPassed']
totalRewards = totalRewards + outcome['totalRewards']
print(totalRewards)
trial +=1
except AssertionError:
break
elif nameAlg == 'RLSVI' or nameAlg =='DRLSVI':
while trial<numTrials:
try:
print (trial)
rl.rlsvi.sigma = 500.0
simulate(ab,rl,numTrials=1, maxIterations=1000, verbose=False, show=False)
rl.rlsvi.sigma = 500.0
outcome = simulate(ab,rl,numTrials=1, maxIterations=1000, verbose=False, show=False)
levelsPassed = levelsPassed + outcome['levelsPassed']
totalRewards = totalRewards + outcome['totalRewards']
print(totalRewards)
trial +=1
except AssertionError:
break
elif nameAlg =='DRLSVI':
while trial<numTrials:
try:
print (trial)
rl.makeRLSVI()
simulate(ab,rl,numTrials=1, maxIterations=1000, verbose=False, show=False)
rl.makeLSVI(0.0)
outcome = simulate(ab,rl,numTrials=1, maxIterations=1000, verbose=False, show=False)
levelsPassed = levelsPassed + outcome['levelsPassed']
totalRewards = totalRewards + outcome['totalRewards']
print(totalRewards)
trial +=1
except AssertionError:
break
cumlevelsPassed = np.cumsum(levelsPassed)/range(1,len(levelsPassed)+1)
cumtotalRewards = np.cumsum(totalRewards)/range(1,len(totalRewards)+1)
with open('../results/levelsPassed_'+name,'wb') as file:
pickle.dump(levelsPassed,file)
with open('../results/cumlevelsPassed_'+name,'wb') as file:
pickle.dump(cumlevelsPassed,file)
with open('../results/totalRewards_'+name,'wb') as file:
pickle.dump(totalRewards,file)
with open('../results/cumtotalRewards_'+name,'wb') as file:
pickle.dump(cumtotalRewards,file)
print('Max Total Rewards _'+name,max(totalRewards))
trials = range(1,len(cumlevelsPassed)+1)
plt.figure()
plt.plot(trials,cumlevelsPassed,lw=2,color='blue')
plt.scatter(trials,cumlevelsPassed)
plt.xlabel('Number of trials',fontsize='large')
plt.ylabel('Number of levels passed',fontsize='large')
plt.title('Rewards per trials', fontsize=20)
maxLevel=0
for i in range(len(cumlevelsPassed)):
if levelsPassed[i]>maxLevel:
maxLevel=levelsPassed[i]
plt.axvline(i+1,color='r', linestyle='--')
plt.savefig('../plots/levelsPassed__'+name+'.png')
plt.figure()
plt.plot(trials,cumtotalRewards,lw=2,color='green')
plt.scatter(trials,cumtotalRewards)
plt.xlabel('Number of trials',fontsize='large')
plt.ylabel('Number of total rewards',fontsize='large')
plt.title('Rewards per trials', fontsize=20)
maxLevel=0
for i in range(len(cumlevelsPassed)):
if levelsPassed[i]>maxLevel:
maxLevel=levelsPassed[i]
plt.axvline(i+1,color='r', linestyle='--')
plt.savefig('../plots/totalRewards_'+name+'.png')
rl = rlAlgorithm(actions=agent.getAngryBirdsActions,featureExtractor=featureExtractor,\
epsilon=epsilon)
trainingOutcomes = simulate(ab_train,rl,numTrials=30, maxIterations=1000, verbose=False, show=False)
testOutcomes = simulate(ab_test,rl,numTrials=10, maxIterations=1000, verbose=False, show=False)
with open('../results/train_'+name,'wb') as file:
pickle.dump(trainingOutcomes,file)
with open('../results/test_'+name,'wb') as file:
pickle.dump(testOutcomes,file)
if __name__ == '__main__':
agent = angryAgent()
evaluator(QLearningAlgorithm,agent.PPFeatureExtractor,'Q','PP',multiple = 1.0,numTrials=50, epsilon = 0.3, sigma = 500)
evaluator(QLearningAlgorithm,agent.NPPFeatureExtractor,'Q','NPP',multiple = 1.0,numTrials=50, epsilon = 0.3, sigma = 500)
evaluator(QLearningAlgorithm,agent.NPPOFeatureExtractor,'Q','NPPO',multiple = 1.0,numTrials=50, epsilon = 0.3, sigma = 500)
evaluator(QLearningAlgorithm,agent.NPPSFeatureExtractor,'Q','NPPS',multiple = 1.0,numTrials=50, epsilon = 0.3, sigma = 500)
evaluator(DRLSVI,agent.NPPFeatureExtractor,'DRLSVI','NPP',multiple = 1.0,numTrials=50, epsilon = 0.0, sigma = 500)
evaluator(RLSVI,agent.NPPFeatureExtractor,'LSVI','NPP',multiple = 1.0,numTrials=50, epsilon = 0.3, sigma = 500)
evaluator(QLearningAlgorithm,agent.NPPFeatureExtractor,'Q','NPP',multiple = 2.0,numTrials=50, epsilon = 0.3, sigma = 500)
evaluator(RLSVI,agent.NPPFeatureExtractor,'RLSVI','NPP',multiple = 2.0,numTrials=50, epsilon = 0.0, sigma = 500)
evaluator(RLSVI,agent.NPPFeatureExtractor,'LSVI','NPP',multiple = 2.0,numTrials=50, epsilon = 0.3, sigma = 500)
evaluator(QLearningAlgorithm,agent.NPPFeatureExtractor,'Q','NPP',multiple = 2.0,numTrials=50, epsilon = 0.3, sigma = 500)
generalize_evaluator(RLSVI,agent.NPPFeatureExtractor,'RLSVI','NPP',multiple=1.0,numTrials=50, epsilon = 0.3, sigma = 500)
generalize_evaluator(QLearningAlgorithm,agent.NPPFeatureExtractor,'Q','NPP',multiple=1.0,numTrials=50, epsilon = 0.3, sigma = 500)
trials = range(1,42)
