def train_ppo():
log_dir = f"model_save/"
env = ENV_CONTINUE(istest=False)
env = Monitor(env, log_dir)
env = DummyVecEnv([lambda: env])
# env = VecNormalize(env, norm_obs=True, norm_reward=True,
# clip_obs=10.)
model = PPO('MlpPolicy', env, verbose=1, batch_size=2048, seed=1)
callback = SaveOnBestTrainingRewardCallback(check_freq=480, log_dir=log_dir)
model.learn(total_timesteps=int(1000000), callback = callback, log_interval = 480)
model.save('model_save/ppo')
def train_td3():
log_dir = f"model_save/"
env = ENV_CONTINUE(istest=False)
env = Monitor(env, log_dir)
env = DummyVecEnv([lambda: env])
# env = VecNormalize(env, norm_obs=True, norm_reward=True,
# clip_obs=10.)
n_actions = env.action_space.shape[-1]
action_noise = NormalActionNoise(mean=np.zeros(n_actions),
sigma=0.1 * np.ones(n_actions))
# model = TD3("MlpPolicy", env, action_noise=action_noise, verbose=1, batch_size=2048, seed=1)
model = TD3('MlpPolicy', env, verbose=1, batch_size=2048, seed=1)
callback = SaveOnBestTrainingRewardCallback(check_freq=480,
log_dir=log_dir)
model.learn(total_timesteps=int(100000),
callback=callback,
log_interval=100)
model.save('model_save/td3')
def test_ppo():
log_dir = f"model_save/best_model_ppo"
env = ENV_CONTINUE(istest=True)
env.render = True
env = Monitor(env, log_dir)
model = PPO.load(log_dir)
plot_results(f"model_save/")
for i in range(10):
state = env.reset()
day = 0
while True:
action = model.predict(state)
next_state, reward, done, info = env.step(action[0])
state = next_state
# print("trying:",day,"reward:", reward,"now profit:",env.profit)
day+=1
if done:
print('stock',i,' total profit=',env.profit,' buy hold=',env.buy_hold)
break