def test_l1_l2_norm(self):
env = make('Acrobot-v1')
env_spec = EnvSpec(obs_space=env.observation_space,
action_space=env.action_space)
name = 'dqn'
mlp_q = MLPQValueFunction(env_spec=env_spec,
name_scope=name + '_mlp',
name=name + '_mlp',
mlp_config=[{
"ACT": "RELU",
"B_INIT_VALUE": 0.0,
"NAME": "1",
"N_UNITS": 16,
"TYPE": "DENSE",
"W_NORMAL_STDDEV": 0.03,
"L1_NORM": 1000.0,
"L2_NORM": 1000.0
}, {
"ACT": "LINEAR",
"B_INIT_VALUE": 0.0,
"NAME": "OUPTUT",
"N_UNITS": 1,
"L1_NORM": 1000.0,
"L2_NORM": 1000.0,
"TYPE": "DENSE",
"W_NORMAL_STDDEV": 0.03
}])
dqn = DQN(env_spec=env_spec,
config_or_config_dict=dict(REPLAY_BUFFER_SIZE=1000,
GAMMA=0.99,
BATCH_SIZE=10,
LEARNING_RATE=0.01,
TRAIN_ITERATION=1,
DECAY=0.5),
name=name,
value_func=mlp_q)
dqn2, _ = self.create_dqn(name='dqn_2')
a = TransitionData(env_spec)
st = env.reset()
dqn.init()
dqn2.init()
for i in range(100):
ac = dqn.predict(obs=st, sess=self.sess, batch_flag=False)
st_new, re, done, _ = env.step(action=ac)
a.append(state=st,
new_state=st_new,
action=ac,
done=done,
reward=re)
st = st_new
dqn.append_to_memory(a)
for i in range(20):
print(
'dqn1 loss: ',
dqn.train(batch_data=a,
train_iter=10,
sess=None,
update_target=True))
print(
'dqn2 loss: ',
dqn2.train(batch_data=a,
train_iter=10,
sess=None,
update_target=True))
var_list = self.sess.run(dqn.q_value_func.parameters('tf_var_list'))
print(var_list)
var_list2 = self.sess.run(dqn2.q_value_func.parameters('tf_var_list'))
print(var_list2)
for var, var2 in zip(var_list, var_list2):
diff = np.abs(var2) - np.abs(var)
self.assertTrue(np.greater(np.mean(diff), 0.0).all())
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)
dqn.init()
st = env.reset()
from baconian.common.sampler.sample_data import TransitionData
a = TransitionData(env_spec)
res = []
for i in range(100):
ac = dqn.predict(obs=st, sess=self.sess, batch_flag=False)
st_new, re, done, _ = env.step(action=ac)
a.append(state=st,
new_state=st_new,
action=ac,
done=done,
reward=re)
dqn.append_to_memory(a)
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['log_val']
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)
Logger().flush_recorder()
