def q_learning_nfq(**args):
# estimate
best_score = 0
best_turn = 1000
best_agent = None
score_list = []
turn_list = []
#for i in range(2):
for i in range(50):
agent = QLearning(12, 4)
# training
agent.greedy_rate = 0.0
print
print "==========================="
print 'before training'
print_state(agent.get_q_values)
training(agent, args)
print 'after training'
print_state(agent.get_q_values)
agent.greedy_rate = 0.7
#agent.learner._setExplorer(EpsilonGreedyExplorer(0.3))
score, turn = play(agent, 'neural', args, [2, 2])
score_list.append(score)
turn_list.append(turn)
print
print 'test one play'
print i, int(numpy.mean(score_list)), max(score_list), score, turn
if best_agent == None or numpy.average(
best_agent.train_error) > numpy.average(agent.train_error):
print 'best train error !'
best_score = score
best_turn = turn
best_agent = agent
# if best_score < score or best_turn > turn:
# print 'best train error !'
# best_score = score
# best_turn = turn
# best_agent = agent
with open(args['path'] + '/result.dump', 'w') as f:
pickle.dump([score_list, turn_list, best_agent], f)
print
print "==========================="
print 'best score : ', best_score
print 'best turn : ', best_turn
print_state(best_agent.get_q_values)
def q_learning_nfq(**args):
# estimate
best_score = 0
best_turn = 1000
best_agent = None
score_list = []
turn_list = []
#for i in range(2):
for i in range(50):
agent = QLearning(12, 4)
# training
agent.greedy_rate = 0.0
print
print "==========================="
print 'before training'
print_state(agent.get_q_values)
training(agent, args)
print 'after training'
print_state(agent.get_q_values)
agent.greedy_rate = 0.7
#agent.learner._setExplorer(EpsilonGreedyExplorer(0.3))
score, turn = play(agent, 'neural', args, [2,2])
score_list.append(score)
turn_list.append(turn)
print
print 'test one play'
print i, int(numpy.mean(score_list)) , max(score_list) , score, turn
if best_agent==None or numpy.average(best_agent.train_error) > numpy.average(agent.train_error):
print 'best train error !'
best_score = score
best_turn = turn
best_agent = agent
# if best_score < score or best_turn > turn:
# print 'best train error !'
# best_score = score
# best_turn = turn
# best_agent = agent
with open(args['path']+'/result.dump', 'w') as f:
pickle.dump([score_list, turn_list, best_agent], f)
print
print "==========================="
print 'best score : ', best_score
print 'best turn : ', best_turn
print_state(best_agent.get_q_values)
def q_learning_nfq(**args):
# estimate
best_score = 0
best_turn = 1000
best_agent = None
score_list = []
turn_list = []
for i in range(1):
#for i in range(50):
#agent = QLearning(12, 4)
agent = QLearning(117, 4)
# training
agent.greedy_rate = 0.5
for i in range(100):
print
print "=========================== ", i
agent.greedy_rate += 0.05 if agent.greedy_rate < 0.7 else 0.0
training(agent, args)
agent.greedy_rate = 0.7
#score, turn = play(agent, 'neural', args, [2,2])
score, turn = play(agent)
score_list.append(score)
turn_list.append(turn)
print
print 'test one play'
print i, int(numpy.mean(score_list)), max(score_list), score, turn
if best_agent == None or numpy.average(
best_agent.train_error) > numpy.average(agent.train_error):
print 'best train error !'
best_score = score
best_turn = turn
best_agent = agent
# if best_score < score or best_turn > turn:
# print 'best train error !'
# best_score = score
# best_turn = turn
# best_agent = agent
with open(args['path'] + '/result.dump', 'w') as f:
pickle.dump([score_list, turn_list, best_agent], f)
print
print "==========================="
print 'best score : ', best_score
print 'best turn : ', best_turn
def q_learning_nfq(**args):
# estimate
best_score = 0
best_turn = 1000
best_agent = None
score_list = []
turn_list = []
for i in range(1):
#for i in range(50):
#agent = QLearning(12, 4)
agent = QLearning(117, 4)
# training
agent.greedy_rate = 0.5
for i in range(100):
print
print "=========================== ", i
agent.greedy_rate += 0.05 if agent.greedy_rate < 0.7 else 0.0
training(agent, args)
agent.greedy_rate = 0.7
#score, turn = play(agent, 'neural', args, [2,2])
score, turn = play(agent)
score_list.append(score)
turn_list.append(turn)
print
print 'test one play'
print i, int(numpy.mean(score_list)) , max(score_list) , score, turn
if best_agent==None or numpy.average(best_agent.train_error) > numpy.average(agent.train_error):
print 'best train error !'
best_score = score
best_turn = turn
best_agent = agent
# if best_score < score or best_turn > turn:
# print 'best train error !'
# best_score = score
# best_turn = turn
# best_agent = agent
with open(args['path']+'/result.dump', 'w') as f:
pickle.dump([score_list, turn_list, best_agent], f)
print
print "==========================="
print 'best score : ', best_score
print 'best turn : ', best_turn
