def test_sample_batch_as_Transition_2(self):
env = make('ModifiedHalfCheetah')
env.init()
env_spec = env.env_spec
random_buffer = TransitionData(env_spec=env_spec, obs_shape=env_spec.obs_shape, \
action_shape=env_spec.action_shape, size=100)
print("====> Random Sample")
num_trajectory = 1
max_step = 100
for i in range(num_trajectory):
ep_len = 0
obs = env.reset()
while ep_len < max_step:
act = self.RandomController_get_action(env=env, state=obs)
obs_, reward, done, _ = env.step(act)
random_buffer.append(obs, act, obs_, done, reward)
assert not done
obs = obs_
ep_len += 1
batch_data = random_buffer.sample_batch_as_Transition(
batch_size=32, shuffle_flag=True, all_as_batch=True)
self.assertEqual(len(batch_data), 4)
self.assertEqual(len(batch_data[-1]), 4)
def test_StandScaler(self):
env = make('ModifiedHalfCheetah')
env_spec = env.env_spec
self.assertEqual(env_spec.flat_obs_dim, 18)
self.assertEqual(env_spec.flat_action_dim, 6)
buffer_size = 10
buffer = TransitionData(env_spec=env_spec, obs_shape=env_spec.obs_shape, \
action_shape=env_spec.action_shape, size=buffer_size)
obs = env.reset()
for i in range(buffer_size):
act = env.action_space.sample()
obs_, rew, done, _ = env.step(act)
buffer.append(obs, act, obs_, done, rew)
batch_list = buffer.sample_batch_as_Transition(4, all_as_batch=True)
state_input_scaler_1 = RunningStandardScaler(env_spec.flat_action_dim)
for batch_data in batch_list:
state_input_scaler_1.update_scaler(batch_data.action_set)
mean_1 = state_input_scaler_1._mean
var_1 = state_input_scaler_1._var
print(mean_1)
print(var_1)
state_input_scaler_2 = RunningStandardScaler(env_spec.flat_action_dim)
state_input_scaler_2.update_scaler(buffer.action_set)
mean_2 = state_input_scaler_2._mean
var_2 = state_input_scaler_2._var
print(mean_2)
print(var_2)