def test_unstack_actions(self):
num_envs = 5
action_spec = self.action_spec
rng = np.random.RandomState()
batched_action = np.array([
array_spec.sample_bounded_spec(action_spec, rng)
for _ in range(num_envs)
])
# Test that actions are correctly unstacked when just batched in np.array.
unstacked_actions = batched_py_environment.unstack_actions(batched_action)
for action in unstacked_actions:
self.assertAllEqual(action_spec.shape, action.shape)
def step_adversary(self, actions):
if self._num_envs == 1:
actions = nest_utils.unbatch_nested_array(actions)
time_steps = self._envs[0].step_adversary(actions)
return nest_utils.batch_nested_array(time_steps)
else:
unstacked_actions = batched_py_environment.unstack_actions(actions)
if len(unstacked_actions) != self.batch_size:
raise ValueError(
'Primary dimension of action items does not match '
'batch size: %d vs. %d' % (len(unstacked_actions), self.batch_size))
time_steps = self._execute(
lambda env_action: env_action[0].step_adversary(env_action[1]),
zip(self._envs, unstacked_actions))
return nest_utils.stack_nested_arrays(time_steps)
def test_unstack_nested_actions(self):
num_envs = 5
action_spec = self.action_spec
rng = np.random.RandomState()
batched_action = np.array([
array_spec.sample_bounded_spec(action_spec, rng)
for _ in range(num_envs)
])
# Test that actions are correctly unstacked when nested in namedtuple.
class NestedAction(
collections.namedtuple('NestedAction', ['action', 'other_var'])):
pass
nested_action = NestedAction(
action=batched_action, other_var=np.array([13.0] * num_envs))
unstacked_actions = batched_py_environment.unstack_actions(nested_action)
for nested_action in unstacked_actions:
self.assertAllEqual(action_spec.shape, nested_action.action.shape)
self.assertEqual(13.0, nested_action.other_var)