def __init__(<EOL>self,<EOL>states,<EOL>actions,<EOL>network,<EOL>batched_observe=True,<EOL>batching_capacity=<NUM_LIT:1000>,<EOL>scope='<STR_LIT>',<EOL>device=None,<EOL>saver=None,<EOL>summarizer=None,<EOL>execution=None,<EOL>variable_noise=None,<EOL>states_preprocessing=None,<EOL>actions_exploration=None,<EOL>reward_preprocessing=None,<EOL>update_mode=None,<EOL>memory=None,<EOL>optimizer=None,<EOL>discount=<NUM_LIT>,<EOL>distributions=None,<EOL>entropy_regularization=None,<EOL>target_sync_frequency=<NUM_LIT>,<EOL>target_update_weight=<NUM_LIT:1.0>,<EOL>double_q_model=False,<EOL>huber_loss=None<EOL>):
<EOL>if update_mode is None:<EOL><INDENT>update_mode = dict(<EOL>unit='<STR_LIT>',<EOL>batch_size=<NUM_LIT:10><EOL>)<EOL><DEDENT>elif '<STR_LIT>' in update_mode:<EOL><INDENT>assert update_mode['<STR_LIT>'] == '<STR_LIT>'<EOL><DEDENT>else:<EOL><INDENT>update_mode['<STR_LIT>'] = '<STR_LIT>'<EOL><DEDENT>if memory is None:<EOL><INDENT>memory = dict(<EOL>type='<STR_LIT>',<EOL>include_next_states=True,<EOL>capacity=(<NUM_LIT:1000> * update_mode['<STR_LIT>'])<EOL>)<EOL><DEDENT>else:<EOL><INDENT>assert memory['<STR_LIT>']<EOL><DEDENT>if optimizer is None:<EOL><INDENT>optimizer = dict(<EOL>type='<STR_LIT>',<EOL>learning_rate=<NUM_LIT><EOL>)<EOL><DEDENT>self.target_sync_frequency = target_sync_frequency<EOL>self.target_update_weight = target_update_weight<EOL>self.double_q_model = double_q_model<EOL>self.huber_loss = huber_loss<EOL>super(DQNNstepAgent, self).__init__(<EOL>states=states,<EOL>actions=actions,<EOL>batched_observe=batched_observe,<EOL>batching_capacity=batching_capacity,<EOL>scope=scope,<EOL>device=device,<EOL>saver=saver,<EOL>summarizer=summarizer,<EOL>execution=execution,<EOL>variable_noise=variable_noise,<EOL>states_preprocessing=states_preprocessing,<EOL>actions_exploration=actions_exploration,<EOL>reward_preprocessing=reward_preprocessing,<EOL>update_mode=update_mode,<EOL>memory=memory,<EOL>optimizer=optimizer,<EOL>discount=discount,<EOL>network=network,<EOL>distributions=distributions,<EOL>entropy_regularization=entropy_regularization<EOL>)<EOL>
Initializes the DQN n-step agent.

Args:
    update_mode (spec): Update mode specification, with the following attributes:
        - unit: 'episodes' if given (default: 'episodes').
        - batch_size: integer (default: 10).
        - frequency: integer (default: batch_size).
    memory (spec): Memory specification, see core.memories module for more information
        (default: {type='latest', include_next_states=true, capacity=1000*batch_size}).
    optimizer (spec): Optimizer specification, see core.optimizers module for more
        information (default: {type='adam', learning_rate=1e-3}).
    target_sync_frequency (int): Target network sync frequency (default: 10000).
    target_update_weight (float): Target network update weight (default: 1.0).
    double_q_model (bool): Specifies whether double DQN mode is used (default: false).
    huber_loss (float): Huber loss clipping (default: none).