loss=agent.loss)
#print(action, reward)
state = next_state
if done:
print('start', env.start, 'previous', (cash, nown),
'current', tuple(env.holdings))
print("episode: {}/{}, score: {}, e: {:.5}".format(
e, EPISODES, time, agent.epsilon))
print('average_loss =', agent.loss / env.init['span'])
f.write(str(agent.loss) + '\n')
f.flush()
agent.loss = 0
if e % 2 == 0:
grapher.show(action_labels=env.action_labels,
ep=e,
t=time,
e=agent.epsilon)
grapher.reset()
agent.save(save_string)
break
# if len(agent.memory) > batch_size:
# agent.replay(batch_size)
# Test
if e % 2 == 0:
state = test_env.reset()
state = np.reshape(state, [1, state_size])
for time in range(500):
cash, nown, price = test_env.holdings[
0], test_env.holdings[1], test_env.state[-1]
action = agent.act(state, time, is_test=True)
lines = []
updatexy(t)
p[2] = y + 40
p[1] = x
test.update_destination_point(p, s)
lines = to_lines(test.joint_points)
lines2 = to_lines(test.foward_model(test.joint_angle_only))
for _line in lines2:
lines.append(_line)
g.redraw(lines)
# sys.stdout.write("\r" + test.return_model())
print test.return_model_for_low_level()
test.send_serial()
sys.stdout.flush()
test = RoverArm([50, 40, 15])
test.update_destination_point([40, 0, 40], [1, 0, 0])
test.ros_begin()
test.send_serial()
lines = to_lines(test.joint_points)
# test.establish_serial_connection()
# test.serial_write()
g = Grapher(lines)
g.redraw(lines)
while not test.my_rospy.is_shutdown():
g.show(anim)
model.model.fit(x_train, y_train)
y_pred = model.model.predict(x_test)
prev_prices, prices, pred = x_test[:,
-1], y_test, y_pred.reshape(
y_pred.shape[0])
if e % 100 == 0:
for mode, action_labels in modes.items():
trader = Trader(mode)
strategy = trader.to_strategy(prev_prices, pred)
optimal = trader.to_strategy(prev_prices, prices)
trader.run(strategy, prices, grapher=grapher)
# f.write('{} accuracy_score={} balanced_accuracy_score={} average_precision_score={} f1_score={}\n'.format(
# e,
# accuracy_score(strategy, optimal),
# balanced_accuracy_score(strategy, optimal),
# average_precision_score(strategy, optimal),
# f1_score(strategy, optimal)))
grapher.action_labels = action_labels
grapher.pred = pred[:-1]
grapher.show(action_labels=action_labels,
ep=e,
span=span,
w=window,
mode=trader.mode)
grapher.reset()
