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>discount=<NUM_LIT>,<EOL>distributions=None,<EOL>entropy_regularization=None,<EOL>baseline_mode=None,<EOL>baseline=None,<EOL>baseline_optimizer=None,<EOL>gae_lambda=None,<EOL>likelihood_ratio_clipping=None,<EOL>learning_rate=<NUM_LIT>,<EOL>cg_max_iterations=<NUM_LIT:10>,<EOL>cg_damping=<NUM_LIT>,<EOL>cg_unroll_loop=True,<EOL>ls_max_iterations=<NUM_LIT:10>,<EOL>ls_accept_ratio=<NUM_LIT>,<EOL>ls_unroll_loop=False<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>pass<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=False,<EOL>capacity=(<NUM_LIT:1000> * update_mode['<STR_LIT>'])<EOL>)<EOL><DEDENT>else:<EOL><INDENT>assert not memory['<STR_LIT>']<EOL><DEDENT>assert (update_mode['<STR_LIT>'] != '<STR_LIT>' or memory['<STR_LIT:type>'] == '<STR_LIT>')<EOL>optimizer = dict(<EOL>type='<STR_LIT>',<EOL>optimizer=dict(<EOL>type='<STR_LIT>',<EOL>learning_rate=learning_rate,<EOL>cg_max_iterations=cg_max_iterations,<EOL>cg_damping=cg_damping,<EOL>cg_unroll_loop=cg_unroll_loop,<EOL>),<EOL>ls_max_iterations=ls_max_iterations,<EOL>ls_accept_ratio=ls_accept_ratio,<EOL>ls_mode='<STR_LIT>',  <EOL>ls_parameter=<NUM_LIT:0.5>,  <EOL>ls_unroll_loop=ls_unroll_loop<EOL>)<EOL>self.baseline_mode = baseline_mode<EOL>self.baseline = baseline<EOL>self.baseline_optimizer = baseline_optimizer<EOL>self.gae_lambda = gae_lambda<EOL>self.likelihood_ratio_clipping = likelihood_ratio_clipping<EOL>super(TRPOAgent, 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 TRPO 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=false, capacity=1000*batch_size}).
    optimizer (spec): TRPO agent implicitly defines a optimized-step natural-gradient
        optimizer.
    baseline_mode (str): One of 'states', 'network' (default: none).
    baseline (spec): Baseline specification, see core.baselines module for more information
        (default: none).
    baseline_optimizer (spec): Baseline optimizer specification, see core.optimizers module
        for more information (default: none).
    gae_lambda (float): Lambda factor for generalized advantage estimation (default: none).
    likelihood_ratio_clipping (float): Likelihood ratio clipping for policy gradient
        (default: none).
    learning_rate (float): Learning rate of natural-gradient optimizer (default: 1e-3).
    cg_max_iterations (int): Conjugate-gradient max iterations (default: 20).
    cg_damping (float): Conjugate-gradient damping (default: 1e-3).
    cg_unroll_loop (bool): Conjugate-gradient unroll loop (default: false).
    ls_max_iterations (int): Line-search max iterations (default: 10).
    ls_accept_ratio (float): Line-search accept ratio (default: 0.9).
    ls_unroll_loop (bool): Line-search unroll loop (default: false).