def main(args):
for ite in range(int(args['trial_num'])):
print('Trial Number:', ite)
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
# config = tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False)
# with tf.Session(config=config) as sess:
with tf.Session() as sess:
if args['change_seed']:
rand_seed = 10 * ite
#rand_seed = np.random.randint(1, 1000, size=1)
else:
rand_seed = 0
env = gym.make(args['env'])
np.random.seed(int(args['random_seed']) + int(rand_seed))
tf.set_random_seed(int(args['random_seed']) + int(rand_seed))
env.seed(int(args['random_seed']) + int(rand_seed))
env_test = gym.make(args['env'])
env_test.seed(int(args['random_seed']) + int(rand_seed))
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.shape[0]
print('action_space.shape', env.action_space.shape)
print('observation_space.shape', env.observation_space.shape)
action_bound = env.action_space.high
assert (env.action_space.high[0] == -env.action_space.low[0])
agent = adInfoHRLTD3(sess,
env,
state_dim,
action_dim,
action_bound,
int(args['minibatch_size']),
tau=float(args['tau']),
actor_lr=float(args['actor_lr']),
critic_lr=float(args['critic_lr']),
option_lr=float(args['option_lr']),
gamma=float(args['gamma']),
hidden_dim=np.asarray(args['hidden_dim']),
entropy_coeff=float(args['lambda']),
c_reg=float(args['c_reg']),
option_num=int(args['option_num']),
vat_noise=float(args['vat_noise']),
c_ent=float(args['c_ent']))
if args['use_gym_monitor']:
if not args['render_env']:
env = wrappers.Monitor(env,
args['monitor_dir'],
video_callable=False,
force=True)
else:
env = wrappers.Monitor(
env,
args['monitor_dir'],
video_callable=lambda episode_id: episode_id % 50 == 0,
force=True)
step_R_i = train(sess, env, env_test, args, agent)
result_path = "./results/trials/separate/"
try:
import pathlib
pathlib.Path(result_path).mkdir(parents=True, exist_ok=True)
except:
print(
"A result directory does not exist and cannot be created. The trial results are not saved"
)
result_filename = args['result_file'] + '_' + args['env'] \
+ '_lambda_' + str(float(args['lambda'])) \
+ '_c_reg_' + str(float(args['c_reg'])) \
+ '_vat_noise_' + str(float(args['vat_noise'])) \
+ '_c_ent_' + str(float(args['c_ent'])) \
+ '_option_' + str(float(args['option_num'])) \
+ '_temporal_' + str(float(args['temporal_num'])) \
+ '_trial_idx_' + str(int(args['trial_idx'])) \
+ '.txt'
if args['overwrite_result'] and ite == 0:
np.savetxt(result_filename, np.asarray(step_R_i))
else:
data = np.loadtxt(result_filename, dtype=float)
data_new = np.vstack((data, np.asarray(step_R_i)))
np.savetxt(result_filename, data_new)
if args['use_gym_monitor']:
env.monitor.close()
def main(args):
result_name = 'adInfoHRLTD3_' + args['env'] \
+ '_lambda_' + str(float(args['lambda'])) \
+ '_c_reg_' + str(float(args['c_reg'])) \
+ '_vat_noise_' + str(float(args['vat_noise'])) \
+ '_c_ent_' + str(float(args['c_ent'])) \
+ '_option_' + str(float(args['option_num'])) \
+ '_temporal_' + str(float(args['temporal_num'])) \
+ '_trial_idx_' + str(int(args['trial_idx']))
for ite in range(int(args['trial_num'])):
print('Trial Number:', ite)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
config = tf.ConfigProto(gpu_options=gpu_options,
log_device_placement=False)
with tf.Session(config=config) as sess:
if args['change_seed']:
rand_seed = 10 * ite
else:
rand_seed = 0
np.random.seed(int(args['random_seed']) + int(rand_seed))
tf.set_random_seed(int(args['random_seed']) + int(rand_seed))
env = gym.make(args['env'])
env.seed(int(args['random_seed']) + int(rand_seed))
if args['save_video']:
try:
import pathlib
pathlib.Path("./Video/" + args['env']).mkdir(parents=True,
exist_ok=True)
video_relative_path = "./Video/" + args['env'] + "/"
## To save video of the first episode
env = gym.wrappers.Monitor(
env,
video_relative_path,
video_callable=lambda episode_id: episode_id == 0,
force=True)
## To save video of every episodes
# env_test = gym.wrappers.Monitor(env_test, video_relative_path, \
# video_callable=lambda episode_id: episode_id%1==0, force =True)
except:
print(
"Cannot create video directories. Video will not be saved."
)
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.shape[0]
action_bound = env.action_space.high
# Ensure action bound is symmetric
assert (env.action_space.high[0] == -env.action_space.low[0])
if args['method_name'] == 'adInfoHRLTD3':
from adInfoHRL_agent import adInfoHRLTD3
agent = adInfoHRLTD3(
sess,
env,
state_dim,
action_dim,
action_bound,
# , int(args['minibatch_size']),
# tau=float(args['tau']),
# actor_lr=float(args['actor_lr']),
# critic_lr=float(args['critic_lr']),
# gamma=float(args['gamma']),
# hidden_dim=np.asarray(args['hidden_dim']),
# entropy_coeff=float(args['lambda']),
# c_reg=float(args['c_reg']),
option_num=int(args['option_num']),
# vat_noise=float(args['vat_noise'])
)
model_path = "./Model/adInfoHRL/" + args['env'] + '/'
agent.load_model(iteration=int(args['load_model_iter']),
expname=result_name,
model_path=model_path)
test(sess, env, args, agent, result_name)
if args['use_gym_monitor']:
env.close()