dodox/fancy_gym
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

add prodmp to test_black_box

Browse Source
This commit is contained in:
Hongyi Zhou
2022-11-09 17:54:34 +01:00
parent ffe48dfb57
commit 60e18d2964
6 changed files with 108 additions and 28 deletions
Download Patch File Download Diff File Expand all files Collapse all files
test/test_black_box.py
+23 -14
View File
@@ -67,28 +67,32 @@ def test_missing_wrapper(env_id: str):
fancy_gym.make_bb(env_id, [], {}, {}, {}, {}, {})
@pytest.mark.parametrize('mp_type', ['promp', 'dmp'])
@pytest.mark.parametrize('mp_type', ['promp', 'dmp', 'prodmp'])
def test_missing_local_state(mp_type: str):
basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
env = fancy_gym.make_bb('toy-v0', [RawInterfaceWrapper], {},
{'trajectory_generator_type': mp_type},
{'controller_type': 'motor'},
{'phase_generator_type': 'exp'},
{'basis_generator_type': 'rbf'})
{'basis_generator_type': basis_generator_type})
env.reset()
with pytest.raises(NotImplementedError):
env.step(env.action_space.sample())
@pytest.mark.parametrize('mp_type', ['promp', 'dmp'])
@pytest.mark.parametrize('mp_type', ['promp', 'dmp', 'prodmp'])
@pytest.mark.parametrize('env_wrap', zip(ENV_IDS, WRAPPERS))
@pytest.mark.parametrize('verbose', [1, 2])
def test_verbosity(mp_type: str, env_wrap: Tuple[str, Type[RawInterfaceWrapper]], verbose: int):
basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
env_id, wrapper_class = env_wrap
env = fancy_gym.make_bb(env_id, [wrapper_class], {'verbose': verbose},
{'trajectory_generator_type': mp_type},
{'controller_type': 'motor'},
{'phase_generator_type': 'exp'},
{'basis_generator_type': 'rbf'})
{'basis_generator_type': basis_generator_type})
env.reset()
info_keys = list(env.step(env.action_space.sample())[3].keys())
@@ -104,15 +108,17 @@ def test_verbosity(mp_type: str, env_wrap: Tuple[str, Type[RawInterfaceWrapper]]
assert all(e in info_keys for e in mp_keys)
@pytest.mark.parametrize('mp_type', ['promp', 'dmp'])
@pytest.mark.parametrize('mp_type', ['promp', 'dmp', 'prodmp'])
@pytest.mark.parametrize('env_wrap', zip(ENV_IDS, WRAPPERS))
def test_length(mp_type: str, env_wrap: Tuple[str, Type[RawInterfaceWrapper]]):
basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
env_id, wrapper_class = env_wrap
env = fancy_gym.make_bb(env_id, [wrapper_class], {},
{'trajectory_generator_type': mp_type},
{'controller_type': 'motor'},
{'phase_generator_type': 'exp'},
{'basis_generator_type': 'rbf'})
{'basis_generator_type': basis_generator_type})
for _ in range(5):
env.reset()
@@ -121,14 +127,15 @@ def test_length(mp_type: str, env_wrap: Tuple[str, Type[RawInterfaceWrapper]]):
assert length == env.spec.max_episode_steps
@pytest.mark.parametrize('mp_type', ['promp', 'dmp'])
@pytest.mark.parametrize('mp_type', ['promp', 'dmp', 'prodmp'])
@pytest.mark.parametrize('reward_aggregation', [np.sum, np.mean, np.median, lambda x: np.mean(x[::2])])
def test_aggregation(mp_type: str, reward_aggregation: Callable[[np.ndarray], float]):
basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
env = fancy_gym.make_bb('toy-v0', [ToyWrapper], {'reward_aggregation': reward_aggregation},
{'trajectory_generator_type': mp_type},
{'controller_type': 'motor'},
{'phase_generator_type': 'exp'},
{'basis_generator_type': 'rbf'})
{'basis_generator_type': basis_generator_type})
env.reset()
# ToyEnv only returns 1 as reward
assert env.step(env.action_space.sample())[1] == reward_aggregation(np.ones(50, ))
@@ -149,12 +156,13 @@ def test_context_space(mp_type: str, env_wrap: Tuple[str, Type[RawInterfaceWrapp
assert env.observation_space.shape == wrapper.context_mask[wrapper.context_mask].shape
@pytest.mark.parametrize('mp_type', ['promp', 'dmp'])
@pytest.mark.parametrize('mp_type', ['promp', 'dmp', 'prodmp'])
@pytest.mark.parametrize('num_dof', [0, 1, 2, 5])
@pytest.mark.parametrize('num_basis', [0, 1, 2, 5])
@pytest.mark.parametrize('num_basis', [0, 2, 5]) # should add 1 back after the bug is fixed
@pytest.mark.parametrize('learn_tau', [True, False])
@pytest.mark.parametrize('learn_delay', [True, False])
def test_action_space(mp_type: str, num_dof: int, num_basis: int, learn_tau: bool, learn_delay: bool):
basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
env = fancy_gym.make_bb('toy-v0', [ToyWrapper], {},
{'trajectory_generator_type': mp_type,
'action_dim': num_dof
@@ -164,28 +172,29 @@ def test_action_space(mp_type: str, num_dof: int, num_basis: int, learn_tau: boo
'learn_tau': learn_tau,
'learn_delay': learn_delay
},
{'basis_generator_type': 'rbf',
{'basis_generator_type': basis_generator_type,
'num_basis': num_basis
})
base_dims = num_dof * num_basis
additional_dims = num_dof if mp_type == 'dmp' else 0
additional_dims = num_dof if 'dmp' in mp_type else 0
traj_modification_dims = int(learn_tau) + int(learn_delay)
assert env.action_space.shape[0] == base_dims + traj_modification_dims + additional_dims
@pytest.mark.parametrize('mp_type', ['promp', 'dmp'])
@pytest.mark.parametrize('mp_type', ['promp', 'dmp', 'prodmp'])
@pytest.mark.parametrize('a', [1])
@pytest.mark.parametrize('b', [1.0])
@pytest.mark.parametrize('c', [[1], [1.0], ['str'], [{'a': 'b'}], [np.ones(3, )]])
@pytest.mark.parametrize('d', [{'a': 1}, {1: 2.0}, {'a': [1.0]}, {'a': np.ones(3, )}, {'a': {'a': 'b'}}])
@pytest.mark.parametrize('e', [Object()])
def test_change_env_kwargs(mp_type: str, a: int, b: float, c: list, d: dict, e: Object):
basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
env = fancy_gym.make_bb('toy-v0', [ToyWrapper], {},
{'trajectory_generator_type': mp_type},
{'controller_type': 'motor'},
{'phase_generator_type': 'exp'},
{'basis_generator_type': 'rbf'},
{'basis_generator_type': basis_generator_type},
a=a, b=b, c=c, d=d, e=e
)
assert a is env.a