def test_env_config(self):
env = suite_highway.load(environment_name="highway-v0")
self.assertEqual(env.observation_spec().shape, (35, ))
self.assertTrue(env.action_spec().is_continuous)
self.assertEqual(env.action_spec().shape, (2, ))
env.close()
# test env with specified config
env_config = {
"observation": {
"type": "Kinematics",
"vehicles_count": 3,
"features":
["presence", "x", "y", "vx", "vy", "cos_h", "sin_h"],
},
"action": {
"type": "DiscreteMetaAction"
}
}
env = suite_highway.load(environment_name="highway-v0",
env_config=env_config)
self.assertEqual(env.observation_spec().shape, (21, ))
self.assertTrue(env.action_spec().is_discrete)
self.assertEqual(env.action_spec().numel, 1)
actions = env.action_spec().sample().cpu().numpy()
for _ in range(10):
time_step = env.step(actions)
self._env = env
def test_last_step(self, max_episode_steps):
env_config = {
"observation": {
"type": "Kinematics",
"vehicles_count": 3,
"features":
["presence", "x", "y", "vx", "vy", "cos_h", "sin_h"],
},
"action": {
"type": "DiscreteMetaAction"
},
"duration": max_episode_steps
}
self._env = suite_highway.load(environment_name="highway-v0",
env_config=env_config)
for i in range(max_episode_steps):
actions = self._env.action_spec().sample().cpu().numpy()
time_step = self._env.step(actions)
if time_step.step_type == 2:
break
if time_step.env_info['crashed'].item() is True:
assert time_step.discount == 0.0
if i == max_episode_steps - 1 and not time_step.env_info[
'crashed'].item():
assert time_step.discount == 1.0
def test_env_info(self):
self._env = suite_highway.load(environment_name="highway-v0")
actions = self._env.action_spec().sample()
time_step = self._env.step(actions.cpu().numpy())
env_info = time_step.env_info
for field in env_info:
self.assertEqual(env_info[field].size, 1)
self.assertFalse('action' in time_step.env_info.keys())
def test_thread_env(self):
self._env = thread_environment.ThreadEnvironment(
lambda: suite_highway.load(environment_name='highway-v0'))
self.assertIsInstance(self._env, alf_environment.AlfEnvironment)
self.assertEqual(torch.float32, self._env.observation_spec().dtype)
actions = self._env.action_spec().sample()
for _ in range(10):
time_step = self._env.step(actions)
def test_unwrapped_env(self):
self._env = suite_highway.load(environment_name='highway-v0')
self.assertIsInstance(self._env, alf_environment.AlfEnvironment)
self.assertEqual(torch.float32, self._env.observation_spec().dtype)
actions = self._env.action_spec().sample()
for _ in range(10):
# unwrapped env (not thread_env or parallel_env) needs to convert
# from tensor to array
time_step = self._env.step(actions.cpu().numpy())
def ctor(env_id=None):
return suite_highway.load(environment_name='highway-v0')