def test_gray_scale_observation(env_id, keep_dim):
gray_env = AtariPreprocessing(gym.make(env_id),
screen_size=84,
grayscale_obs=True)
rgb_env = AtariPreprocessing(gym.make(env_id),
screen_size=84,
grayscale_obs=False)
wrapped_env = GrayScaleObservation(rgb_env, keep_dim=keep_dim)
assert rgb_env.observation_space.shape[-1] == 3
seed = 0
gray_env.seed(seed)
wrapped_env.seed(seed)
gray_obs = gray_env.reset()
wrapped_obs = wrapped_env.reset()
if keep_dim:
assert wrapped_env.observation_space.shape[-1] == 1
assert len(wrapped_obs.shape) == 3
wrapped_obs = wrapped_obs.squeeze(-1)
else:
assert len(wrapped_env.observation_space.shape) == 2
assert len(wrapped_obs.shape) == 2
# ALE gray scale is slightly different, but no more than by one shade
assert np.allclose(gray_obs.astype("int32"),
wrapped_obs.astype("int32"),
atol=1)
def test_atari_preprocessing_grayscale(env_fn):
import cv2
env1 = env_fn()
env2 = AtariPreprocessing(env_fn(),
screen_size=84,
grayscale_obs=True,
frame_skip=1,
noop_max=0)
env3 = AtariPreprocessing(env_fn(),
screen_size=84,
grayscale_obs=False,
frame_skip=1,
noop_max=0)
env1.seed(0)
env2.seed(0)
env3.seed(0)
obs1 = env1.reset()
obs2 = env2.reset()
obs3 = env3.reset()
assert obs1.shape == (210, 160, 3)
assert obs2.shape == (84, 84)
assert obs3.shape == (84, 84, 3)
assert np.allclose(
obs3, cv2.resize(obs1, (84, 84), interpolation=cv2.INTER_AREA))
obs3_gray = cv2.cvtColor(obs3, cv2.COLOR_RGB2GRAY)
# the edges of the numbers do not render quite the same in the grayscale, so we ignore them
assert np.allclose(obs2[10:38], obs3_gray[10:38])
# the paddle also do not render quite the same
assert np.allclose(obs2[44:], obs3_gray[44:])
env1.close()
env2.close()
env3.close()
def main(args):
if args.cpu_only == True:
cpu = tf.config.experimental.list_physical_devices(device_type='CPU')
tf.config.experimental.set_visible_devices(devices=cpu, device_type='CPU')
# random seed setting
if args.random_seed <= 0:
random_seed = np.random.randint(1, 9999)
else:
random_seed = args.random_seed
tf.random.set_seed(random_seed)
np.random.seed(random_seed)
random.seed(random_seed)
#env setting
if args.domain_type == 'gym':
#openai gym
env = gym.make(args.env_name)
env.seed(random_seed)
env.action_space.seed(random_seed)
test_env = gym.make(args.env_name)
test_env.seed(random_seed)
test_env.action_space.seed(random_seed)
elif args.domain_type == 'dmc':
#deepmind control suite
env = dmc2gym.make(domain_name=args.env_name.split('/')[0], task_name=args.env_name.split('/')[1], seed=random_seed)
test_env = dmc2gym.make(domain_name=args.env_name.split('/')[0], task_name=args.env_name.split('/')[1], seed=random_seed)
elif args.domain_type == 'atari':
#openai gym
env = gym.make(args.env_name)
env = AtariPreprocessing(env, frame_skip=args.frame_skip, screen_size=args.image_size, grayscale_newaxis=True)
env = FrameStack(env, args.frame_stack)
env._max_episode_steps = 10000
env.seed(random_seed)
env.action_space.seed(random_seed)
test_env = gym.make(args.env_name)
test_env = AtariPreprocessing(test_env, frame_skip=args.frame_skip, screen_size=args.image_size, grayscale_newaxis=True)
test_env._max_episode_steps = 10000
test_env = FrameStack(test_env, args.frame_stack)
test_env.seed(random_seed)
test_env.action_space.seed(random_seed)
state_dim = env.observation_space.shape[0]
if args.domain_type == 'atari':
state_dim = env.observation_space.shape
action_dim = env.action_space.n
max_action = 1
min_action = 1
if args.domain_type is 'gym':
algorithm = DQN(state_dim, action_dim, args)
elif args.domain_type is 'dmc':
algorithm = DQN(state_dim, action_dim, args)
elif args.domain_type == 'atari':
algorithm = ImageDQN(state_dim, action_dim, args)
print("Training of", env.unwrapped.spec.id)
print("Algorithm:", algorithm.name)
print("State dim:", state_dim)
print("Action dim:", action_dim)
trainer = Basic_trainer(env, test_env, algorithm, max_action, min_action, args)
trainer.run()
