def __init__(self, *args, name='trpo_maml', step_size=0.01, inner_type='likelihood_ratio', exploration=False, **kwargs):
    super(TRPOMAML, self).__init__(*args, **kwargs)
    assert (inner_type in ['log_likelihood', 'likelihood_ratio', 'dice'])
    self.step_size = step_size
    self.inner_type = inner_type
    self.name = name
    self._optimization_keys = ['observations', 'actions', 'advantages', 'agent_infos']
    self.exploration = exploration
    if exploration:
        self._optimization_keys.append('adj_avg_rewards')
    self.optimizer = ConjugateGradientOptimizer()
    self.build_graph()
def __init__(self, *args, name='trpo_maml', step_size=0.01, inner_type='likelihood_ratio', exploration=False, **kwargs):
    super(TRPOMAML, self).__init__(*args, **kwargs)
    assert (inner_type in ['log_likelihood', 'likelihood_ratio', 'dice'])
    self.step_size = step_size
    self.inner_type = inner_type
    self.name = name
    self._optimization_keys = ['observations', 'actions', 'advantages', 'agent_infos']
    self.exploration = exploration
    if exploration:
        self._optimization_keys.append('adj_avg_rewards')
    self.optimizer = ConjugateGradientOptimizer()
    self.build_graph()