def __init__(<EOL>self,<EOL>states,<EOL>actions,<EOL>scope,<EOL>device,<EOL>saver,<EOL>summarizer,<EOL>execution,<EOL>batching_capacity,<EOL>variable_noise,<EOL>states_preprocessing,<EOL>actions_exploration,<EOL>reward_preprocessing,<EOL>update_mode,<EOL>memory,<EOL>optimizer,<EOL>discount<EOL>):
self.update_mode = update_mode<EOL>self.memory_spec = memory<EOL>self.optimizer_spec = optimizer<EOL>assert discount is None or discount >= <NUM_LIT:0.0><EOL>self.discount = discount<EOL>self.memory = None<EOL>self.optimizer = None<EOL>self.fn_discounted_cumulative_reward = None<EOL>self.fn_reference = None<EOL>self.fn_loss_per_instance = None<EOL>self.fn_regularization_losses = None<EOL>self.fn_loss = None<EOL>self.fn_optimization = None<EOL>self.fn_import_experience = None<EOL>super(MemoryModel, self).__init__(<EOL>states=states,<EOL>actions=actions,<EOL>scope=scope,<EOL>device=device,<EOL>saver=saver,<EOL>summarizer=summarizer,<EOL>execution=execution,<EOL>batching_capacity=batching_capacity,<EOL>variable_noise=variable_noise,<EOL>states_preprocessing=states_preprocessing,<EOL>actions_exploration=actions_exploration,<EOL>reward_preprocessing=reward_preprocessing<EOL>)<EOL>
Memory model.

Args:
    states (spec): The state-space description dictionary.
    actions (spec): The action-space description dictionary.
    scope (str): The root scope str to use for tf variable scoping.
    device (str): The name of the device to run the graph of this model on.
    saver (spec): Dict specifying whether and how to save the model's parameters.
    summarizer (spec): Dict specifying which tensorboard summaries should be created and added to the graph.
    execution (spec): Dict specifying whether and how to do distributed training on the model's graph.
    batching_capacity (int): Batching capacity.
    variable_noise (float): The stddev value of a Normal distribution used for adding random
        noise to the model's output (for each batch, noise can be toggled and - if active - will be resampled).
        Use None for not adding any noise.
    states_preprocessing (spec / dict of specs): Dict specifying whether and how to preprocess state signals
        (e.g. normalization, greyscale, etc..).
    actions_exploration (spec / dict of specs): Dict specifying whether and how to add exploration to the model's
        "action outputs" (e.g. epsilon-greedy).
    reward_preprocessing (spec): Dict specifying whether and how to preprocess rewards coming
        from the Environment (e.g. reward normalization).
    update_mode (spec): Update mode.
    memory (spec): Memory.
    optimizer (spec): Dict specifying the tf optimizer to use for tuning the model's trainable parameters.
    discount (float): The RL reward discount factor (gamma).