def __init__()

in level_replay/storage.py [0:0]

• 23 lines of code
• 4 McCabe index (conditional complexity)

    def __init__(self, num_steps, num_processes, obs_shape, action_space,
                 recurrent_hidden_state_size, split_ratio=0.05):
        self.obs = torch.zeros(num_steps + 1, num_processes, *obs_shape)
        self.recurrent_hidden_states = torch.zeros(
            num_steps + 1, num_processes, recurrent_hidden_state_size)
        self.rewards = torch.zeros(num_steps, num_processes, 1)
        self.value_preds = torch.zeros(num_steps + 1, num_processes, 1)
        self.returns = torch.zeros(num_steps + 1, num_processes, 1)
        self.action_log_probs = torch.zeros(num_steps, num_processes, 1)
        self.action_log_dist = torch.zeros(num_steps, num_processes, action_space.n)
        if action_space.__class__.__name__ == 'Discrete':
            action_shape = 1
        else:
            action_shape = action_space.shape[0]
        self.actions = torch.zeros(num_steps, num_processes, action_shape)
        if action_space.__class__.__name__ == 'Discrete':
            self.actions = self.actions.long()
        self.masks = torch.ones(num_steps + 1, num_processes, 1)

        # Masks that indicate whether it's a true terminal state
        # or time limit end state
        self.bad_masks = torch.ones(num_steps + 1, num_processes, 1)

        self.level_seeds = torch.zeros(num_steps, num_processes, 1, dtype=torch.int)

        self.num_steps = num_steps
        self.step = 0
        
        self.split_ratio = split_ratio