def watch_result(episode, s_time, e_time, c_index, all_index, action, reward,
profit):
print('-------------------- Check -------------------------')
print('start time: ' + s_time)
print('counter : ', c_index, '/', all_index, ' of episode : ', episode,
'/', EPISODES)
print('action : ', action)
print('current profit : ', profit * MARGIN)
print('reward (all profit): ', reward)
print('end_time: ' + e_time)
print('-------------------End Check -----------------------')
if __name__ == "__main__":
agent = DQNAgent(state_size)
#agent.load("agent_model.h5")
num_index = all_index - state_size
env = TrainEnvironment(X_train, num_index)
batch_size = 32
for e in range(EPISODES):
state = env.reset()
state = np.reshape(state, (1, state_size, 1))
for t in range(end_index - start_index):
start_time = str(datetime.datetime.now().time())
action = agent.act(state)
next_state, reward, done = env.step(action)
next_state = np.reshape(next_state, (1, state_size, 1))
agent.remember(state, action, reward, next_state, done)
state = next_state
def watch_result(episode, s_time, e_time, c_index, all_index, action, reward,
profit):
print('-------------------- Check -------------------------')
print('start time: ' + s_time)
print('counter : ', c_index, '/', all_index, ' of episode : ', episode,
'/', EPISODES)
print('action : ', action)
print('current profit : ', profit * MARGIN)
print('reward (all profit): ', reward)
print('end_time: ' + e_time)
print('-------------------End Check -----------------------')
if __name__ == "__main__":
agent = DQNAgent(state_size)
agent.load("agent_model.h5")
num_index = all_index - state_size
env = TrainEnvironment(X_train, num_index)
batch_size = 3
test_profit = []
for e in range(EPISODES):
state = env.reset()
state = np.reshape(state, (1, state_size, 1))
test_profit = []
for t in range(end_index - start_index):
start_time = str(datetime.datetime.now().time())
action = agent.act(state, False) # test
next_state, reward, done = env.step(action)
next_state = np.reshape(next_state, (1, state_size, 1))