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 = 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
