price,
action,
reward,
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])
