def test_vec_env(tmp_path, make_env):
"""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)
if isinstance(obs, dict):
for key in obs.keys():
assert np.max(np.abs(obs[key])) <= clip_obs
else:
assert np.max(np.abs(obs)) <= clip_obs
assert np.max(np.abs(rew)) <= clip_reward
path = tmp_path / "vec_normalize"
norm_venv.save(path)
deserialized = VecNormalize.load(path, venv=orig_venv)
check_vec_norm_equal(norm_venv, deserialized)
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)
env,
policy_kwargs=policy_kwargs,
n_steps=4000,
verbose=1,
batch_size=10000,
n_epochs=4)
if not (os.path.exists(policy_save_dir)):
os.makedirs(policy_save_dir)
# model = PPO('MlpPolicy', env, policy_kwargs=policy_kwargs, verbose=1, n_steps=2048)
# model.set_parameters(os.path.join(policy_save_dir, 'ppo_model_6_S_PV_4096_3000w_21-03-2021_02-50-06.zip'))
t1 = time.time()
model.learn(total_timesteps=12000000)
print(f"The training spent {time.time() - t1} s.")
model.save(policy_save_path)
env.save(env_stats_path)
else:
# env = env_change_input(time_step=env_params['time_step'],
# robot_class=env_params['robot_class'],
# on_rack=env_params['on_rack'],
# enable_self_collision=env_params['enable_self_collision'],
# motor_control_mode=env_params['motor_control_mode'],
# train_or_test=env_params['train_or_test'])
# env = env_change_input(**env_params)
env = SubprocVecEnv([lambda: env_change_input(**env_params)])
env_stats_load_path = os.path.join(
policy_save_dir, 'ppo_env_8_S_PV_4096_12w_21-03-2021_20-46-02.pkl')
env = VecNormalize.load(env_stats_load_path, env)
env.training = False
env.norm_reward = False
