cash, nown, price = env.holdings[0], env.holdings[
1], env.state[-1]
# env.render()
action = agent.act(state, time)
next_state, reward, done, _ = env.step(action)
next_state = np.reshape(next_state, [1, state_size])
agent.remember(state, action, reward, next_state, done)
# agent.train(state, action, reward, next_state, done)
if len(agent.memory) > batch_size:
agent.replay(batch_size)
if e % 2 == 0:
#cash, nown, price = state[0, 1], state[0, 2], state[0, -1]
# cash, nown, price = *env.holdings, state[0,-1]
grapher.add(cash,
nown,
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:
