def __init__(self, model_dir, num_execution_per_step, base_env=None, img_dim=48):
self.predictive_model = PredictiveModel(model_dir)
self.base_env = base_env
self.img_dim = img_dim
self.num_execution_per_step = num_execution_per_step
self.past_length = self.predictive_model.past_length
self.state_dim = self.predictive_model.state_dim ## should be smaller than 11
self.past = np.zeros([self.past_length, self.state_dim])
self._set_action_space()
self.observation_space = base_env.observation_space
def __init__(self,
model_dir,
num_execution_per_step,
base_env=None,
img_dim=48):
## base_env is assumed to be passed in, predictive_model is loaded here
self.predictive_model = PredictiveModel(model_dir)
self.base_env = base_env
self.img_dim = img_dim
self.num_execution_per_step = num_execution_per_step
self.past_length = self.predictive_model.past_length
self.state_dim = self.predictive_model.state_dim ## should be smaller than 11
self.past = np.zeros([self.past_length, self.state_dim])
self._set_action_space()
self.observation_space = base_env.observation_space
# record z and trajs distribution
from datetime import datetime
now = datetime.now()
curr_time = now.strftime("%H-%M-%S")
self.file_z = open('file_z_{}.txt'.format(curr_time), 'w')
self.file_action = open('file_action_{}.txt'.format(curr_time), 'w')
self.file_obs = open('file_obs_{}.txt'.format(curr_time), 'w')