def get_config(self): config = super(SarsaAgent, self).get_config() config['nb_actions'] = self.nb_actions config['gamma'] = self.gamma config['nb_steps_warmup'] = self.nb_steps_warmup config['train_interval'] = self.train_interval config['delta_clip'] = self.delta_clip config['model'] = get_object_config(self.model) config['policy'] = get_object_config(self.policy) config['test_policy'] = get_object_config(self.test_policy) return config
def get_config(self): config = { 'nb_actions': self.nb_actions, 'gamma': self.gamma, 'batch_size': self.batch_size, 'nb_steps_warmup': self.nb_steps_warmup, 'train_interval': self.train_interval, 'memory_interval': self.memory_interval, 'target_model_update': self.target_model_update, 'delta_clip': self.delta_clip, 'memory': get_object_config(self.memory), 'enable_double_dqn': self.enable_double_dqn, 'nb_samples_policy': self.nb_samples_policy, 'nb_sampled_quantiles': self.nb_sampled_quantiles, 'model': get_object_config(self.model), 'policy': get_object_config(self.policy), 'test_policy': get_object_config(self.test_policy), } if self.compiled: config['target_model'] = get_object_config(self.target_model) return config
def get_config(self): """Return configurations of LinearAnnealedPolicy # Returns Dict of config """ config = super(ModifiedLinearAnnealedPolicy, self).get_config() config['attr'] = self.attr config['value_max'] = self.value_max config['value_min'] = self.value_min config['value_test'] = self.value_test config['nb_steps'] = self.nb_steps config['inner_policy'] = get_object_config(self.inner_policy) return config