def validate(self, agent: Agent, *args, **kwargs):
old_result = self.result
self.validation_result = 0
for a in range(len(self._dynamics_model)):
individual_model = self._dynamics_model.model[a]
env = individual_model.return_as_env()
batch_data = agent.sample(
env=env,
sample_count=self.parameters('validation_trajectory_count'),
sample_type='trajectory',
store_flag=False)
self.result[a] = batch_data.get_mean_of('reward')
if self.result[a] > old_result[a]:
self.validation_result += 1
self.validation_result = self.validation_result / len(
self._dynamics_model)
return self.validation_result
def test_integration_with_dqn(self):
env = make('Acrobot-v1')
env_spec = EnvSpec(obs_space=env.observation_space,
action_space=env.action_space)
mlp_q = MLPQValueFunction(env_spec=env_spec,
name='mlp_q',
name_scope='mlp_q',
mlp_config=[
{
"ACT": "RELU",
"B_INIT_VALUE": 0.0,
"NAME": "1",
"N_UNITS": 16,
"TYPE": "DENSE",
"W_NORMAL_STDDEV": 0.03
},
{
"ACT": "LINEAR",
"B_INIT_VALUE": 0.0,
"NAME": "OUPTUT",
"N_UNITS": 1,
"TYPE": "DENSE",
"W_NORMAL_STDDEV": 0.03
}
])
dqn = DQN(env_spec=env_spec,
name='dqn_test',
config_or_config_dict=dict(REPLAY_BUFFER_SIZE=1000,
GAMMA=0.99,
BATCH_SIZE=10,
LEARNING_RATE=0.001,
TRAIN_ITERATION=1,
DECAY=0.5),
value_func=mlp_q)
agent = Agent(env=env, env_spec=env_spec,
algo=dqn,
name='agent')
agent.init()
# dqn.init()
st = env.reset()
from baconian.common.sampler.sample_data import TransitionData
a = TransitionData(env_spec)
res = []
agent.sample(env=env,
sample_count=100,
in_which_status='TRAIN',
store_flag=True,
sample_type='transition')
agent.sample(env=env,
sample_count=100,
in_which_status='TRAIN',
store_flag=True,
sample_type='transition')
res.append(dqn.train(batch_data=a, train_iter=10, sess=None, update_target=True)['average_loss'])
res.append(dqn.train(batch_data=None, train_iter=10, sess=None, update_target=True)['average_loss'])
self.assertTrue(dqn in dqn.recorder._obj_log)
self.assertTrue('average_loss' in dqn.recorder._obj_log[dqn])
self.assertTrue(len(dqn.recorder._obj_log[dqn]['average_loss']) == 2)
self.assertTrue(
np.equal(np.array(res), [x['value'] for x in dqn.recorder._obj_log[dqn]['average_loss']]).all())
self.assertTrue(len(Logger()._registered_recorders) > 0)
self.assertTrue(dqn.recorder in Logger()._registered_recorders)
res = dqn.recorder.get_log(attr_name='average_loss', filter_by_status=dict())
self.assertEqual(len(res), 2)
res = agent.recorder.get_log(attr_name='sum_reward', filter_by_status={'status': 'TRAIN'})
self.assertEqual(len(res), 2)
res = agent.recorder.get_log(attr_name='sum_reward', filter_by_status={'status': 'TEST'})
self.assertEqual(len(res), 0)
Logger().flush_recorder()
