330 lines
13 KiB
Python
330 lines
13 KiB
Python
from itertools import chain
|
|
from typing import Tuple, Type, Union, Optional, Callable
|
|
|
|
import gym
|
|
import numpy as np
|
|
import pytest
|
|
from gym import register
|
|
from gym.core import ActType, ObsType
|
|
|
|
import fancy_gym
|
|
from fancy_gym.black_box.raw_interface_wrapper import RawInterfaceWrapper
|
|
from fancy_gym.utils.time_aware_observation import TimeAwareObservation
|
|
|
|
SEED = 1
|
|
ENV_IDS = ['Reacher5d-v0', 'dmc:ball_in_cup-catch', 'metaworld:reach-v2', 'Reacher-v2']
|
|
WRAPPERS = [fancy_gym.envs.mujoco.reacher.MPWrapper, fancy_gym.dmc.suite.ball_in_cup.MPWrapper,
|
|
fancy_gym.meta.goal_object_change_mp_wrapper.MPWrapper, fancy_gym.open_ai.mujoco.reacher_v2.MPWrapper]
|
|
ALL_MP_ENVS = chain(*fancy_gym.ALL_MOVEMENT_PRIMITIVE_ENVIRONMENTS.values())
|
|
|
|
|
|
class Object(object):
|
|
pass
|
|
|
|
|
|
class ToyEnv(gym.Env):
|
|
observation_space = gym.spaces.Box(low=-1, high=1, shape=(1,), dtype=np.float64)
|
|
action_space = gym.spaces.Box(low=-1, high=1, shape=(1,), dtype=np.float64)
|
|
dt = 0.02
|
|
|
|
def __init__(self, a: int = 0, b: float = 0.0, c: list = [], d: dict = {}, e: Object = Object()):
|
|
self.a, self.b, self.c, self.d, self.e = a, b, c, d, e
|
|
|
|
def reset(self, *, seed: Optional[int] = None, return_info: bool = False,
|
|
options: Optional[dict] = None) -> Union[ObsType, Tuple[ObsType, dict]]:
|
|
return np.array([-1])
|
|
|
|
def step(self, action: ActType) -> Tuple[ObsType, float, bool, dict]:
|
|
return np.array([-1]), 1, False, {}
|
|
|
|
def render(self, mode="human"):
|
|
pass
|
|
|
|
|
|
class ToyWrapper(RawInterfaceWrapper):
|
|
|
|
@property
|
|
def current_pos(self) -> Union[float, int, np.ndarray, Tuple]:
|
|
return np.ones(self.action_space.shape)
|
|
|
|
@property
|
|
def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
|
|
return np.zeros(self.action_space.shape)
|
|
|
|
|
|
@pytest.fixture(scope="session", autouse=True)
|
|
def setup():
|
|
register(
|
|
id=f'toy-v0',
|
|
entry_point='test.test_black_box:ToyEnv',
|
|
max_episode_steps=50,
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize('env_id', ENV_IDS)
|
|
def test_missing_wrapper(env_id: str):
|
|
with pytest.raises(ValueError):
|
|
fancy_gym.make_bb(env_id, [], {}, {}, {}, {}, {})
|
|
|
|
|
|
@pytest.mark.parametrize('mp_type', ['promp', 'dmp'])
|
|
def test_missing_local_state(mp_type: str):
|
|
env = fancy_gym.make_bb('toy-v0', [RawInterfaceWrapper], {},
|
|
{'trajectory_generator_type': mp_type},
|
|
{'controller_type': 'motor'},
|
|
{'phase_generator_type': 'exp'},
|
|
{'basis_generator_type': 'rbf'})
|
|
env.reset()
|
|
with pytest.raises(NotImplementedError):
|
|
env.step(env.action_space.sample())
|
|
|
|
|
|
@pytest.mark.parametrize('mp_type', ['promp', 'dmp'])
|
|
@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):
|
|
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'})
|
|
env.reset()
|
|
info_keys = list(env.step(env.action_space.sample())[3].keys())
|
|
|
|
env_step = fancy_gym.make(env_id, SEED)
|
|
env_step.reset()
|
|
info_keys_step = env_step.step(env_step.action_space.sample())[3].keys()
|
|
|
|
assert all(e in info_keys for e in info_keys_step)
|
|
assert 'trajectory_length' in info_keys
|
|
|
|
if verbose >= 2:
|
|
mp_keys = ['positions', 'velocities', 'step_actions', 'step_observations', 'step_rewards']
|
|
assert all(e in info_keys for e in mp_keys)
|
|
|
|
|
|
@pytest.mark.parametrize('mp_type', ['promp', 'dmp'])
|
|
@pytest.mark.parametrize('env_wrap', zip(ENV_IDS, WRAPPERS))
|
|
def test_length(mp_type: str, env_wrap: Tuple[str, Type[RawInterfaceWrapper]]):
|
|
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'})
|
|
|
|
for _ in range(5):
|
|
env.reset()
|
|
length = env.step(env.action_space.sample())[3]['trajectory_length']
|
|
|
|
assert length == env.spec.max_episode_steps
|
|
|
|
|
|
@pytest.mark.parametrize('mp_type', ['promp', 'dmp'])
|
|
@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]):
|
|
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'})
|
|
env.reset()
|
|
# ToyEnv only returns 1 as reward
|
|
assert env.step(env.action_space.sample())[1] == reward_aggregation(np.ones(50, ))
|
|
|
|
|
|
@pytest.mark.parametrize('mp_type', ['promp', 'dmp'])
|
|
@pytest.mark.parametrize('env_wrap', zip(ENV_IDS, WRAPPERS))
|
|
def test_context_space(mp_type: str, env_wrap: Tuple[str, Type[RawInterfaceWrapper]]):
|
|
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'})
|
|
# check if observation space matches with the specified mask values which are true
|
|
env_step = fancy_gym.make(env_id, SEED)
|
|
wrapper = wrapper_class(env_step)
|
|
assert env.observation_space.shape == wrapper.context_mask[wrapper.context_mask].shape
|
|
|
|
|
|
@pytest.mark.parametrize('mp_type', ['promp', 'dmp'])
|
|
@pytest.mark.parametrize('num_dof', [0, 1, 2, 5])
|
|
@pytest.mark.parametrize('num_basis', [0, 1, 2, 5])
|
|
@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):
|
|
env = fancy_gym.make_bb('toy-v0', [ToyWrapper], {},
|
|
{'trajectory_generator_type': mp_type,
|
|
'action_dim': num_dof
|
|
},
|
|
{'controller_type': 'motor'},
|
|
{'phase_generator_type': 'exp',
|
|
'learn_tau': learn_tau,
|
|
'learn_delay': learn_delay
|
|
},
|
|
{'basis_generator_type': 'rbf',
|
|
'num_basis': num_basis
|
|
})
|
|
|
|
base_dims = num_dof * num_basis
|
|
additional_dims = num_dof if mp_type == 'dmp' 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('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):
|
|
env = fancy_gym.make_bb('toy-v0', [ToyWrapper], {},
|
|
{'trajectory_generator_type': mp_type},
|
|
{'controller_type': 'motor'},
|
|
{'phase_generator_type': 'exp'},
|
|
{'basis_generator_type': 'rbf'},
|
|
a=a, b=b, c=c, d=d, e=e
|
|
)
|
|
assert a is env.a
|
|
assert b is env.b
|
|
assert c is env.c
|
|
# Due to how gym works dict kwargs need to be copied and hence can only be checked to have the same content
|
|
assert d == env.d
|
|
assert e is env.e
|
|
|
|
|
|
@pytest.mark.parametrize('mp_type', ['promp'])
|
|
@pytest.mark.parametrize('tau', [0.25, 0.5, 0.75, 1])
|
|
def test_learn_tau(mp_type: str, tau: float):
|
|
env = fancy_gym.make_bb('toy-v0', [ToyWrapper], {'verbose': 2},
|
|
{'trajectory_generator_type': mp_type,
|
|
},
|
|
{'controller_type': 'motor'},
|
|
{'phase_generator_type': 'linear',
|
|
'learn_tau': True,
|
|
'learn_delay': False
|
|
},
|
|
{'basis_generator_type': 'rbf',
|
|
}, seed=SEED)
|
|
|
|
d = True
|
|
for i in range(5):
|
|
if d:
|
|
env.reset()
|
|
action = env.action_space.sample()
|
|
action[0] = tau
|
|
|
|
obs, r, d, info = env.step(action)
|
|
|
|
length = info['trajectory_length']
|
|
assert length == env.spec.max_episode_steps
|
|
|
|
tau_time_steps = int(np.round(tau / env.dt))
|
|
|
|
pos = info['positions'].flatten()
|
|
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])
|
|
|
|
# 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('delay', [0, 0.25, 0.5, 0.75])
|
|
def test_learn_delay(mp_type: str, delay: float):
|
|
env = fancy_gym.make_bb('toy-v0', [ToyWrapper], {'verbose': 2},
|
|
{'trajectory_generator_type': mp_type,
|
|
},
|
|
{'controller_type': 'motor'},
|
|
{'phase_generator_type': 'linear',
|
|
'learn_tau': False,
|
|
'learn_delay': True
|
|
},
|
|
{'basis_generator_type': 'rbf',
|
|
}, seed=SEED)
|
|
|
|
d = True
|
|
for i in range(5):
|
|
if d:
|
|
env.reset()
|
|
action = env.action_space.sample()
|
|
action[0] = delay
|
|
|
|
obs, r, d, info = env.step(action)
|
|
|
|
length = info['trajectory_length']
|
|
assert length == env.spec.max_episode_steps
|
|
|
|
delay_time_steps = int(np.round(delay / env.dt))
|
|
|
|
pos = info['positions'].flatten()
|
|
vel = info['velocities'].flatten()
|
|
|
|
# Check beginning is all same (only true for linear basis)
|
|
assert np.all(pos[:max(1, delay_time_steps - 1)] == pos[0])
|
|
assert np.all(vel[:max(1, delay_time_steps - 2)] == vel[0])
|
|
|
|
# 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('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):
|
|
env = fancy_gym.make_bb('toy-v0', [ToyWrapper], {'verbose': 2},
|
|
{'trajectory_generator_type': mp_type,
|
|
},
|
|
{'controller_type': 'motor'},
|
|
{'phase_generator_type': 'linear',
|
|
'learn_tau': True,
|
|
'learn_delay': True
|
|
},
|
|
{'basis_generator_type': 'rbf',
|
|
}, seed=SEED)
|
|
|
|
if env.spec.max_episode_steps * env.dt < delay + tau:
|
|
return
|
|
|
|
d = True
|
|
for i in range(5):
|
|
if d:
|
|
env.reset()
|
|
action = env.action_space.sample()
|
|
action[0] = tau
|
|
action[1] = delay
|
|
|
|
obs, r, d, info = env.step(action)
|
|
|
|
length = info['trajectory_length']
|
|
assert length == env.spec.max_episode_steps
|
|
|
|
tau_time_steps = int(np.round(tau / env.dt))
|
|
delay_time_steps = int(np.round(delay / env.dt))
|
|
joint_time_steps = delay_time_steps + tau_time_steps
|
|
|
|
pos = info['positions'].flatten()
|
|
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])
|
|
|
|
# Check beginning is all same (only true for linear basis)
|
|
assert np.all(pos[:delay_time_steps - 1] == pos[0])
|
|
assert np.all(vel[:delay_time_steps - 2] == vel[0])
|
|
|
|
# Check active trajectory section is different to beginning and end values
|
|
active_pos = pos[delay_time_steps: joint_time_steps - 1]
|
|
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])
|