114 lines
4.0 KiB
Python
114 lines
4.0 KiB
Python
import os
|
|
from copy import deepcopy
|
|
|
|
import numpy as np
|
|
import pytest
|
|
import torch as th
|
|
from stable_baselines3.common.identity_env import FakeImageEnv
|
|
from stable_baselines3.common.utils import zip_strict
|
|
|
|
from sb3_contrib import TQC
|
|
|
|
|
|
@pytest.mark.parametrize("model_class", [TQC])
|
|
def test_cnn(tmp_path, model_class):
|
|
SAVE_NAME = "cnn_model.zip"
|
|
# Fake grayscale with frameskip
|
|
# Atari after preprocessing: 84x84x1, here we are using lower resolution
|
|
# to check that the network handle it automatically
|
|
env = FakeImageEnv(screen_height=40, screen_width=40, n_channels=1, discrete=model_class not in {TQC})
|
|
kwargs = {}
|
|
if model_class in {TQC}:
|
|
# Avoid memory error when using replay buffer
|
|
# Reduce the size of the features
|
|
kwargs = dict(buffer_size=250, policy_kwargs=dict(features_extractor_kwargs=dict(features_dim=32)))
|
|
model = model_class("CnnPolicy", env, **kwargs).learn(250)
|
|
|
|
obs = env.reset()
|
|
|
|
action, _ = model.predict(obs, deterministic=True)
|
|
|
|
model.save(tmp_path / SAVE_NAME)
|
|
del model
|
|
|
|
model = model_class.load(tmp_path / SAVE_NAME)
|
|
|
|
# Check that the prediction is the same
|
|
assert np.allclose(action, model.predict(obs, deterministic=True)[0])
|
|
|
|
os.remove(str(tmp_path / SAVE_NAME))
|
|
|
|
|
|
def params_should_match(params, other_params):
|
|
for param, other_param in zip_strict(params, other_params):
|
|
assert th.allclose(param, other_param)
|
|
|
|
|
|
def params_should_differ(params, other_params):
|
|
for param, other_param in zip_strict(params, other_params):
|
|
assert not th.allclose(param, other_param)
|
|
|
|
|
|
@pytest.mark.parametrize("model_class", [TQC])
|
|
@pytest.mark.parametrize("share_features_extractor", [True, False])
|
|
def test_feature_extractor_target_net(model_class, share_features_extractor):
|
|
env = FakeImageEnv(screen_height=40, screen_width=40, n_channels=1, discrete=model_class not in {TQC})
|
|
# Avoid memory error when using replay buffer
|
|
# Reduce the size of the features
|
|
kwargs = dict(
|
|
buffer_size=250,
|
|
learning_starts=100,
|
|
policy_kwargs=dict(
|
|
features_extractor_kwargs=dict(features_dim=32),
|
|
share_features_extractor=share_features_extractor,
|
|
),
|
|
)
|
|
model = model_class("CnnPolicy", env, seed=0, **kwargs)
|
|
|
|
if share_features_extractor:
|
|
# Check that the objects are the same and not just copied
|
|
assert id(model.policy.actor.features_extractor) == id(model.policy.critic.features_extractor)
|
|
else:
|
|
# Check that the objects differ
|
|
assert id(model.policy.actor.features_extractor) != id(model.policy.critic.features_extractor)
|
|
|
|
# Critic and target should be equal at the begginning of training
|
|
params_should_match(model.critic.parameters(), model.critic_target.parameters())
|
|
|
|
model.learn(200)
|
|
|
|
# Critic and target should differ
|
|
params_should_differ(model.critic.parameters(), model.critic_target.parameters())
|
|
|
|
# Re-initialize and collect some random data (without doing gradient steps)
|
|
model = model_class("CnnPolicy", env, seed=0, **kwargs).learn(10)
|
|
|
|
original_param = deepcopy(list(model.critic.parameters()))
|
|
original_target_param = deepcopy(list(model.critic_target.parameters()))
|
|
|
|
# Deactivate copy to target
|
|
model.tau = 0.0
|
|
model.train(gradient_steps=1)
|
|
|
|
# Target should be the same
|
|
params_should_match(original_target_param, model.critic_target.parameters())
|
|
|
|
# not the same for critic net (updated by gradient descent)
|
|
params_should_differ(original_param, model.critic.parameters())
|
|
|
|
# Update the reference as it should not change in the next step
|
|
original_param = deepcopy(list(model.critic.parameters()))
|
|
|
|
# Deactivate learning rate
|
|
model.lr_schedule = lambda _: 0.0
|
|
# Re-activate polyak update
|
|
model.tau = 0.01
|
|
|
|
model.train(gradient_steps=1)
|
|
|
|
# Target should have changed now (due to polyak update)
|
|
params_should_differ(original_target_param, model.critic_target.parameters())
|
|
|
|
# Critic should be the same
|
|
params_should_match(original_param, model.critic.parameters())
|