def main():
window_size = 5
episode_count = 10
stock_name = "^GSPC_2011"
agent = Agent(window_size)
market = Market(window_size=window_size, stock_name=stock_name)
batch_size = 32
start_time = time.time()
for e in range(episode_count + 1):
print("Episodio" + str(e) + "/" + str(episode_count))
agent.reset()
state, price_data = market.reset() # ToDo: get the initial state
for t in range(market.last_data_index):
# obtener acción actual del agente
# llamar al método act() del agente considerando el estado actual
action, bought_price = agent.act(state, price_data)
# obtener siguiente estado del agente según el mercado
next_state, next_price_data, reward, done =\
market.get_next_state_reward(action, bought_price)
# añadir trasacción a la memoria
agent.memory.append((state, action, reward, next_state, done))
# aprender de la historia solo en el caso que haya memoria
if len(agent.memory) > batch_size:
agent.experience_replay(batch_size)
state = next_state
price_data = next_price_data
if done:
print("--------------------------------")
print("Ganancias totales: {0}".format(
agent.get_total_profit()))
print("--------------------------------")
if e % 10 == 0:
if not os.path.exists("models"):
os.mkdir("models")
agent.model.save("models/model_rl" + str(e))
end_time = time.time()
training_time = round(end_time - start_time)
print("Entrenamiento tomó {0} segundos.".format(training_time))
