def __init__(self, path_name, surffix, path_surffix):
    '\n        parameters set\n        '
    self.NUM_NODES = params['number of nodes in the cluster']
    self.env = LraClusterEnv(num_nodes=self.NUM_NODES)
    ckpt_path_1 = (((path_surffix + path_name) + '1') + '/model.ckpt')
    ckpt_path_2 = (((path_surffix + path_name) + '2') + '/model.ckpt')
    ckpt_path_3 = (((path_surffix + path_name) + '3') + '/model.ckpt')
    self.nodes_per_group = int(params['nodes per group'])
    '\n        Build Network\n        '
    self.n_actions = self.nodes_per_group
    self.n_features = int((((self.n_actions * ((self.env.NUM_APPS + 1) + self.env.NUM_APPS)) + 1) + self.env.NUM_APPS))
    self.RL_1 = PolicyGradient(n_actions=self.n_actions, n_features=self.n_features, learning_rate=params['learning rate'], suffix=(surffix + '1a'))
    self.RL_2 = PolicyGradient(n_actions=self.n_actions, n_features=self.n_features, learning_rate=params['learning rate'], suffix=(surffix + '2a'))
    self.RL_3 = PolicyGradient(n_actions=self.n_actions, n_features=self.n_features, learning_rate=params['learning rate'], suffix=(surffix + '3a'))
    self.RL_1.restore_session(ckpt_path_1)
    self.RL_2.restore_session(ckpt_path_2)
    self.RL_3.restore_session(ckpt_path_3)
    (self.observation_episode_1, self.action_episode_1, self.reward_episode_1, self.safety_episode_1) = ([], [], [], [])
    (self.observation_optimal_1, self.action_optimal_1, self.reward_optimal_1, self.safety_optimal_1) = ([], [], [], [])
    (self.observation_episode_2, self.action_episode_2, self.reward_episode_2, self.safety_episode_2) = ([], [], [], [])
    (self.observation_optimal_2, self.action_optimal_2, self.reward_optimal_2, self.safety_optimal_2) = ([], [], [], [])
    (self.observation_episode_3, self.action_episode_3, self.reward_episode_3, self.safety_episode_3) = ([], [], [], [])
    (self.observation_optimal_3, self.action_optimal_3, self.reward_optimal_3, self.safety_optimal_3) = ([], [], [], [])
parameters set
def __init__(self, path_name, surffix, path_surffix):
    '\n        \n        '
    self.NUM_NODES = params['number of nodes in the cluster']
    self.env = LraClusterEnv(num_nodes=self.NUM_NODES)
    ckpt_path_1 = (((path_surffix + path_name) + '1') + '/model.ckpt')
    ckpt_path_2 = (((path_surffix + path_name) + '2') + '/model.ckpt')
    ckpt_path_3 = (((path_surffix + path_name) + '3') + '/model.ckpt')
    self.nodes_per_group = int(params['nodes per group'])
    '\n        Build Network\n        '
    self.n_actions = self.nodes_per_group
    self.n_features = int((((self.n_actions * ((self.env.NUM_APPS + 1) + self.env.NUM_APPS)) + 1) + self.env.NUM_APPS))
    self.RL_1 = PolicyGradient(n_actions=self.n_actions, n_features=self.n_features, learning_rate=params['learning rate'], suffix=(surffix + '1a'))
    self.RL_2 = PolicyGradient(n_actions=self.n_actions, n_features=self.n_features, learning_rate=params['learning rate'], suffix=(surffix + '2a'))
    self.RL_3 = PolicyGradient(n_actions=self.n_actions, n_features=self.n_features, learning_rate=params['learning rate'], suffix=(surffix + '3a'))
    self.RL_1.restore_session(ckpt_path_1)
    self.RL_2.restore_session(ckpt_path_2)
    self.RL_3.restore_session(ckpt_path_3)
    (self.observation_episode_1, self.action_episode_1, self.reward_episode_1, self.safety_episode_1) = ([], [], [], [])
    (self.observation_optimal_1, self.action_optimal_1, self.reward_optimal_1, self.safety_optimal_1) = ([], [], [], [])
    (self.observation_episode_2, self.action_episode_2, self.reward_episode_2, self.safety_episode_2) = ([], [], [], [])
    (self.observation_optimal_2, self.action_optimal_2, self.reward_optimal_2, self.safety_optimal_2) = ([], [], [], [])
    (self.observation_episode_3, self.action_episode_3, self.reward_episode_3, self.safety_episode_3) = ([], [], [], [])
    (self.observation_optimal_3, self.action_optimal_3, self.reward_optimal_3, self.safety_optimal_3) = ([], [], [], [])