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delete hacky experimental codes & add tests to test_black_box

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Hongyi Zhou
2022-11-13 16:59:13 +01:00
parent 60e18d2964
commit 104b909296
3 changed files with 86 additions and 33 deletions
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test/test_black_box.py
+80 -17
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@@ -205,18 +205,20 @@ def test_change_env_kwargs(mp_type: str, a: int, b: float, c: list, d: dict, e:
assert e is env.e
@pytest.mark.parametrize('mp_type', ['promp'])
@pytest.mark.parametrize('mp_type', ['promp', 'prodmp'])
@pytest.mark.parametrize('tau', [0.25, 0.5, 0.75, 1])
def test_learn_tau(mp_type: str, tau: float):
phase_generator_type = 'exp' if mp_type == 'prodmp' else 'linear'
basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
env = fancy_gym.make_bb('toy-v0', [ToyWrapper], {'verbose': 2},
{'trajectory_generator_type': mp_type,
},
{'controller_type': 'motor'},
{'phase_generator_type': 'linear',
{'phase_generator_type': phase_generator_type,
'learn_tau': True,
'learn_delay': False
},
{'basis_generator_type': 'rbf',
{'basis_generator_type': basis_generator_type,
}, seed=SEED)
d = True
@@ -237,26 +239,29 @@ def test_learn_tau(mp_type: str, tau: float):
vel = info['velocities'].flatten()
# Check end is all same (only true for linear basis)
assert np.all(pos[tau_time_steps:] == pos[-1])
assert np.all(vel[tau_time_steps:] == vel[-1])
if phase_generator_type == "linear":
assert np.all(pos[tau_time_steps:] == pos[-1])
assert np.all(vel[tau_time_steps:] == vel[-1])
# Check active trajectory section is different to end values
assert np.all(pos[:tau_time_steps - 1] != pos[-1])
assert np.all(vel[:tau_time_steps - 2] != vel[-1])
@pytest.mark.parametrize('mp_type', ['promp'])
#
#
@pytest.mark.parametrize('mp_type', ['promp', 'prodmp'])
@pytest.mark.parametrize('delay', [0, 0.25, 0.5, 0.75])
def test_learn_delay(mp_type: str, delay: float):
basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
phase_generator_type = 'exp' if mp_type == 'prodmp' else 'linear'
env = fancy_gym.make_bb('toy-v0', [ToyWrapper], {'verbose': 2},
{'trajectory_generator_type': mp_type,
},
{'controller_type': 'motor'},
{'phase_generator_type': 'linear',
{'phase_generator_type': phase_generator_type,
'learn_tau': False,
'learn_delay': True
},
{'basis_generator_type': 'rbf',
{'basis_generator_type': basis_generator_type,
}, seed=SEED)
d = True
@@ -283,21 +288,23 @@ def test_learn_delay(mp_type: str, delay: float):
# Check active trajectory section is different to beginning values
assert np.all(pos[max(1, delay_time_steps):] != pos[0])
assert np.all(vel[max(1, delay_time_steps)] != vel[0])
@pytest.mark.parametrize('mp_type', ['promp'])
#
#
@pytest.mark.parametrize('mp_type', ['promp', 'prodmp'])
@pytest.mark.parametrize('tau', [0.25, 0.5, 0.75, 1])
@pytest.mark.parametrize('delay', [0.25, 0.5, 0.75, 1])
def test_learn_tau_and_delay(mp_type: str, tau: float, delay: float):
phase_generator_type = 'exp' if mp_type == 'prodmp' else 'linear'
basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
env = fancy_gym.make_bb('toy-v0', [ToyWrapper], {'verbose': 2},
{'trajectory_generator_type': mp_type,
},
{'controller_type': 'motor'},
{'phase_generator_type': 'linear',
{'phase_generator_type': phase_generator_type,
'learn_tau': True,
'learn_delay': True
},
{'basis_generator_type': 'rbf',
{'basis_generator_type': basis_generator_type,
}, seed=SEED)
if env.spec.max_episode_steps * env.dt < delay + tau:
@@ -324,8 +331,9 @@ def test_learn_tau_and_delay(mp_type: str, tau: float, delay: float):
vel = info['velocities'].flatten()
# Check end is all same (only true for linear basis)
assert np.all(pos[joint_time_steps:] == pos[-1])
assert np.all(vel[joint_time_steps:] == vel[-1])
if phase_generator_type == "linear":
assert np.all(pos[joint_time_steps:] == pos[-1])
assert np.all(vel[joint_time_steps:] == vel[-1])
# Check beginning is all same (only true for linear basis)
assert np.all(pos[:delay_time_steps - 1] == pos[0])
@@ -336,3 +344,58 @@ def test_learn_tau_and_delay(mp_type: str, tau: float, delay: float):
active_vel = vel[delay_time_steps: joint_time_steps - 2]
assert np.all(active_pos != pos[-1]) and np.all(active_pos != pos[0])
assert np.all(active_vel != vel[-1]) and np.all(active_vel != vel[0])
@pytest.mark.parametrize('mp_type', ['promp', 'prodmp'])
@pytest.mark.parametrize('max_planning_times', [1, 2, 3, 4])
@pytest.mark.parametrize('sub_segment_steps', [5, 10])
def test_replanning_schedule(mp_type: str, max_planning_times: int, sub_segment_steps: int):
basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
phase_generator_type = 'exp' if mp_type == 'prodmp' else 'linear'
env = fancy_gym.make_bb('toy-v0', [ToyWrapper],
{'max_planning_times': max_planning_times,
'replanning_schedule': lambda pos, vel, obs, action, t: t % sub_segment_steps == 0,
'verbose': 2},
{'trajectory_generator_type': mp_type,
},
{'controller_type': 'motor'},
{'phase_generator_type': phase_generator_type,
'learn_tau': False,
'learn_delay': False
},
{'basis_generator_type': basis_generator_type,
},
seed=SEED)
_ = env.reset()
d = False
for i in range(max_planning_times):
_, _, d, _ = env.step(env.action_space.sample())
assert d
@pytest.mark.parametrize('mp_type', ['promp', 'prodmp'])
@pytest.mark.parametrize('max_planning_times', [1, 2, 3, 4])
@pytest.mark.parametrize('sub_segment_steps', [5, 10])
def test_max_planning_times(mp_type: str, max_planning_times: int, sub_segment_steps: int):
basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
phase_generator_type = 'exp' if mp_type == 'prodmp' else 'linear'
env = fancy_gym.make_bb('toy-v0', [ToyWrapper],
{'max_planning_times': max_planning_times,
'replanning_schedule': lambda pos, vel, obs, action, t: t % sub_segment_steps == 0,
'verbose': 2},
{'trajectory_generator_type': mp_type,
},
{'controller_type': 'motor'},
{'phase_generator_type': phase_generator_type,
'learn_tau': False,
'learn_delay': False
},
{'basis_generator_type': basis_generator_type,
},
seed=SEED)
_ = env.reset()
d = False
planning_times = 0
while not d:
_, _, d, _ = env.step(env.action_space.sample())
planning_times += 1
assert planning_times == max_planning_times