def test_vec_env(tmpdir):
"""Test VecNormalize Object"""
clip_obs = 0.5
clip_reward = 5.0
orig_venv = DummyVecEnv([make_env])
norm_venv = VecNormalize(orig_venv,
norm_obs=True,
norm_reward=True,
clip_obs=clip_obs,
clip_reward=clip_reward)
_, done = norm_venv.reset(), [False]
while not done[0]:
actions = [norm_venv.action_space.sample()]
obs, rew, done, _ = norm_venv.step(actions)
assert np.max(np.abs(obs)) <= clip_obs
assert np.max(np.abs(rew)) <= clip_reward
path = str(tmpdir.join("vec_normalize"))
norm_venv.save(path)
deserialized = VecNormalize.load(path, venv=orig_venv)
check_vec_norm_equal(norm_venv, deserialized)
def train(num_timesteps, model_to_load):
try:
env = DummyVecEnv([dsgym])
env = VecNormalize(env)
policy = MlpPolicy
lr = 3e-4 * 0.75
model = PPO2(policy=policy,
env=env,
n_steps=2048,
nminibatches=32,
lam=0.95,
gamma=0.99,
noptepochs=10,
ent_coef=0.01,
learning_rate=linear_schedule(lr),
cliprange=0.2)
if model_to_load:
env = DummyVecEnv([dsgym])
env = VecNormalize.load(
model_to_load.replace(".zip", "vec_normalize.pkl"), env)
model = model.load(model_to_load)
model.set_env(env)
print("Loaded model from: ", model_to_load)
model.set_learning_rate_func(linear_schedule_start_zero(lr))
model.learn(total_timesteps=num_timesteps)
except KeyboardInterrupt:
print("Saving on keyinterrupt")
model.save("D:/openAi/ppo2save/" + time.strftime("%Y_%m_%d-%H_%M_%S"))
# quit
sys.exit()
except BaseException as error:
model.save("D:/openAi/ppo2save/" + time.strftime("%Y_%m_%d-%H_%M_%S"))
print('An exception occurred: {}'.format(error))
traceback.print_exception(*sys.exc_info())
sys.exit()
model.save("D:/openAi/ppo2save/" + time.strftime("%Y_%m_%d-%H_%M_%S"))