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)
domain_name = args.env_name.split('/')[0]
task_name = args.env_name.split('/')[1]
env = dmc2gym.make(domain_name=domain_name,
task_name=task_name,
seed=random_seed,
visualize_reward=False,
from_pixels=True,
height=args.image_size,
width=args.image_size,
frame_skip=args.frame_skip
) #Pre image size for curl, image size for dbc
env = FrameStack(env, k=args.frame_stack)
test_env = dmc2gym.make(domain_name=domain_name,
task_name=task_name,
seed=random_seed,
visualize_reward=False,
from_pixels=True,
height=args.image_size,
width=args.image_size,
frame_skip=args.frame_skip
) #Pre image size for curl, image size for dbc
test_env = FrameStack(test_env, k=args.frame_stack)
state_dim = (3 * args.frame_stack, args.image_size, args.image_size)
action_dim = env.action_space.shape[0]
max_action = env.action_space.high[0]
min_action = env.action_space.low[0]
if args.algorithm == 'SACv1':
algorithm = 0
elif args.algorithm == 'SACv2':
algorithm = SACv2_AE(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)
print("Max action:", max_action)
print("Min action:", min_action)
trainer = Basic_trainer(env, test_env, algorithm, max_action, min_action,
args)
trainer.run()
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)
else:
#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)
if args.discrete == True:
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.n
max_action = 1
min_action = 1
else:
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.shape[0]
max_action = env.action_space.high[0]
min_action = env.action_space.low[0]
algorithm = TRPO(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)
print("Max action:", max_action)
print("Min action:", min_action)
print("Discrete: ", args.discrete)
trainer = Basic_trainer(env, test_env, algorithm, max_action, min_action,
args)
trainer.run()
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()