def test_rendered_env(self):
env = gym_utils.RenderedEnv(SimpleEnv(), resize_to=(64, 12))
obs, _, _, _ = env.step(1)
self.assertTrue(np.allclose(np.zeros([64, 12, 3], np.uint8), obs))
env = gym_utils.RenderedEnv(SimpleEnv(), resize_to=(64, 12),
output_dtype=np.float32)
obs, _, _, _ = env.step(1)
self.assertTrue(np.allclose(np.zeros([64, 12, 3], np.float32), obs))
def test_rendered_env_continuous_2d(self):
env = gym_utils.RenderedEnv(SimpleContinuousActionsEnv(dimensions=2),
resize_to=(64, 12))
obs, _, _, _ = env.step(0.5)
self.assertTrue(np.allclose(np.zeros([64, 12, 3], np.uint8), obs))
env = gym_utils.RenderedEnv(SimpleContinuousActionsEnv(dimensions=2),
resize_to=(64, 12),
output_dtype=np.float32)
obs, _, _, _ = env.step(1)
self.assertTrue(np.allclose(np.zeros([64, 12, 3], np.float32), obs))
def test_action_mapping_2d(self):
num_actions = 8
def expected_continuous_actions(discrete_action):
if discrete_action == [0, 0]:
return np.array([-1, -1])
elif discrete_action == [0, 3]:
return np.array([-1, -0.14285714])
elif discrete_action == [4, 4]:
return np.array([0.14285714, 0.14285714])
elif discrete_action == [7, 7]:
return np.array([1, 1])
discrete_actions = [[0, 0], [0, 3], [4, 4], [7, 7]]
with unittest.mock.patch.object(gym_utils.RenderedEnv,
"step",
autospec=True) as mock_step_method:
env = gym_utils.RenderedEnv(
SimpleContinuousActionsEnv(dimensions=2))
env_discrete = gym_utils.ActionDiscretizeWrapper(env, num_actions)
for discrete_action in discrete_actions:
env_discrete.step(discrete_action)
mock_args, _ = mock_step_method.call_args
np.testing.assert_array_almost_equal(
mock_args[1], expected_continuous_actions(discrete_action))
def test_action_mapping_1d(self):
"""Testing discretization with a mock environment.
In the mock call we get access to the argument of the
SimpleContinuousActionsEnv.step method which we check against
precomputed values of continuous actions.
"""
num_actions = 4
with unittest.mock.patch.object(gym_utils.RenderedEnv,
"step",
autospec=True) as mock_step_method:
env = gym_utils.RenderedEnv(
SimpleContinuousActionsEnv(dimensions=1))
expected_continuous_actions = np.linspace(
np.min(env.action_space.low),
np.min(env.action_space.high),
num=num_actions).flatten()
env_discrete = gym_utils.ActionDiscretizeWrapper(env, num_actions)
for discrete_action in range(num_actions):
env_discrete.step([discrete_action])
mock_step_method.assert_called_with(
unittest.mock.ANY,
expected_continuous_actions[discrete_action])
def test_correct_number_of_discrete_actions_2d(self):
env_discrete = gym_utils.ActionDiscretizeWrapper(gym_utils.RenderedEnv(
SimpleContinuousActionsEnv(dimensions=2)),
num_actions=4)
expected_action_space = gym.spaces.MultiDiscrete([4, 4])
self.assertEqual(env_discrete.action_space, expected_action_space)
def test_correct_number_of_discrete_actions_1d(self):
"""The env should become discrete whenever we pass num_action."""
env_discrete = gym_utils.ActionDiscretizeWrapper(gym_utils.RenderedEnv(
SimpleContinuousActionsEnv(dimensions=1)),
num_actions=4)
expected_action_space = gym.spaces.MultiDiscrete([
4,
])
self.assertEqual(env_discrete.action_space, expected_action_space)
def test_rendered_env(self):
env = gym_utils.RenderedEnv(SimpleEnv(), resize_to=(64, 64))
obs, _, _, _ = env.step(1)
self.assertTrue(np.allclose(np.zeros([64, 64, 3], np.uint8), obs))
