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
def main():
# ダミー変数化のため, [0, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192]
# 入力素子数は, 16×14にされる.
ql_obj = QLearning(16, 4, dummy=False)
max_score = 0
score_list = []
for i in range(10000):
score, result = play(ql_obj)
# Q-learning
ql_obj.train(result)
score_list.append(score)
# print weight
data = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 2], [0, 0, 0, 2]]
output_vec = ql_obj.get_q_values(data)
print i, numpy.mean(score_list), max(score_list), output_vec
def main():
# ダミー変数化のため, [0, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192]
# 入力素子数は, 16×14にされる.
ql_obj = QLearning(16, 4, dummy=False)
max_score = 0
score_list = []
for i in range(10000):
score, result = play(ql_obj)
# Q-learning
ql_obj.train(result)
score_list.append(score)
# print weight
data =[[0,0,0,0], [0,0,0,0], [0,0,0,2], [0,0,0,2]]
output_vec= ql_obj.get_q_values(data)
print i, numpy.mean(score_list) , max(score_list), output_vec
def learning(best_agent):
# print_experience
best_agent.print_experience()
print_state(best_agent.get_q_values)
agent = QLearning(12, 4)
# 学習前
agent.episodes = best_agent.episodes
# 学習
for i, episode in enumerate(best_agent.episodes):
agent.history += episode
if i % 10 == 0 and not i == 0:
agent.learn()
agent.reset()
# 学習前
print_state(agent.get_q_values)
# print_experience
agent.print_experience()
def learning(best_agent):
# print_experience
best_agent.print_experience()
print_state(best_agent.get_q_values)
agent = QLearning(12, 4)
# 学習前
agent.episodes = best_agent.episodes
# 学習
for i, episode in enumerate(best_agent.episodes):
agent.history += episode
if i % 10 == 0 and not i == 0:
agent.learn()
agent.reset()
# 学習前
print_state(agent.get_q_values)
# print_experience
agent.print_experience()
