def test_extra_env_methods_work(self):
cartpole_env = gym.make('CartPole-v1')
env = alf_gym_wrapper.AlfGymWrapper(cartpole_env)
env = alf_wrappers.TimeLimit(env, 2)
self.assertEqual(None, env.get_info())
env.reset()
action = np.array(0, dtype=np.int64)
env.step(action)
self.assertEqual({}, env.get_info())
def test_limit_duration_stops_after_duration(self):
cartpole_env = gym.make('CartPole-v1')
env = alf_gym_wrapper.AlfGymWrapper(cartpole_env)
env = alf_wrappers.TimeLimit(env, 2)
env.reset()
action = np.array(0, dtype=np.int64)
env.step(action)
time_step = env.step(action)
self.assertTrue(time_step.is_last())
self.assertNotEqual(None, time_step.discount)
self.assertNotEqual(0.0, time_step.discount)
def test_limit_duration_wrapped_env_forwards_calls(self):
cartpole_env = gym.spec('CartPole-v1').make()
env = alf_gym_wrapper.AlfGymWrapper(cartpole_env)
env = alf_wrappers.TimeLimit(env, 10)
action_spec = env.action_spec()
self.assertEqual((), action_spec.shape)
self.assertEqual(0, action_spec.minimum)
self.assertEqual(1, action_spec.maximum)
observation_spec = env.observation_spec()
self.assertEqual((4, ), observation_spec.shape)
high = np.array([
4.8,
np.finfo(np.float32).max, 2 / 15.0 * math.pi,
np.finfo(np.float32).max
])
np.testing.assert_array_almost_equal(-high, observation_spec.minimum)
np.testing.assert_array_almost_equal(high, observation_spec.maximum)
def test_automatic_reset(self):
cartpole_env = gym.make('CartPole-v1')
env = alf_gym_wrapper.AlfGymWrapper(cartpole_env)
env = alf_wrappers.TimeLimit(env, 2)
# Episode 1
action = np.array(0, dtype=np.int64)
first_time_step = env.step(action)
self.assertTrue(first_time_step.is_first())
mid_time_step = env.step(action)
self.assertTrue(mid_time_step.is_mid())
last_time_step = env.step(action)
self.assertTrue(last_time_step.is_last())
# Episode 2
first_time_step = env.step(action)
self.assertTrue(first_time_step.is_first())
mid_time_step = env.step(action)
self.assertTrue(mid_time_step.is_mid())
last_time_step = env.step(action)
self.assertTrue(last_time_step.is_last())
def test_duration_applied_after_episode_terminates_early(self):
cartpole_env = gym.make('CartPole-v1')
env = alf_gym_wrapper.AlfGymWrapper(cartpole_env)
env = alf_wrappers.TimeLimit(env, 10000)
# Episode 1 stepped until termination occurs.
action = np.array(1, dtype=np.int64)
time_step = env.step(action)
while not time_step.is_last():
time_step = env.step(action)
self.assertTrue(time_step.is_last())
env._duration = 2
# Episode 2 short duration hits step limit.
action = np.array(0, dtype=np.int64)
first_time_step = env.step(action)
self.assertTrue(first_time_step.is_first())
mid_time_step = env.step(action)
self.assertTrue(mid_time_step.is_mid())
last_time_step = env.step(action)
self.assertTrue(last_time_step.is_last())
