def restore_old(self, policy, val_func, scaler, restore_path):
#mypath = self.checkpoints_dir+"/"+restore_path
mypath = restore_path
print("restoring checkpoint from:", mypath)
from policy import Policy
from value_function import NNValueFunction
policy = Policy(policy.obs_dim,
policy.act_dim,
policy.kl_targ,
restore_flag=True)
with policy.g.as_default():
print("0000000A")
Checkpoint.dump_vars(policy.g)
tf.saved_model.loader.load(policy.sess,
[tf.saved_model.tag_constants.TRAINING],
mypath + ".policy")
print("1111111A")
Checkpoint.dump_vars(policy.g)
policy._placeholders()
print("YYYY:", policy.obs_ph)
val_func = NNValueFunction(val_func.obs_dim, restore_flag=True)
with val_func.g.as_default():
print("2222222A")
Checkpoint.dump_vars(val_func.g)
tf.saved_model.loader.load(val_func.sess,
[tf.saved_model.tag_constants.TRAINING],
mypath + ".val_func")
print("3333333A")
Checkpoint.dump_vars(val_func.g)
val_func._placeholders()
print("YYYY:", val_func.obs_ph)
# unpickle and restore scaler
with open(mypath + ".scaler", 'rb') as f:
(scaler, episode) = pickle.load(f)
print("FINISHED RESTORE")
return (policy, val_func, scaler, episode)
