369 lines
15 KiB
Python
369 lines
15 KiB
Python
from itertools import chain
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from typing import Tuple, Type, Union, Optional, Callable
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import gymnasium as gym
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import numpy as np
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import pytest
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from gymnasium import register, make
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from gymnasium.core import ActType, ObsType
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import fancy_gym
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from fancy_gym.black_box.raw_interface_wrapper import RawInterfaceWrapper
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from fancy_gym.utils.wrappers import TimeAwareObservation
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SEED = 1
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ENV_IDS = ['fancy/Reacher5d-v0', 'dm_control/ball_in_cup-catch-v0', 'metaworld/reach-v2', 'Reacher-v2']
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WRAPPERS = [fancy_gym.envs.mujoco.reacher.MPWrapper, fancy_gym.dmc.suite.ball_in_cup.MPWrapper,
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fancy_gym.meta.goal_object_change_mp_wrapper.MPWrapper, fancy_gym.open_ai.mujoco.reacher_v2.MPWrapper]
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ALL_MP_ENVS = fancy_gym.ALL_MOVEMENT_PRIMITIVE_ENVIRONMENTS['all']
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MAX_STEPS_FALLBACK = 100
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class Object(object):
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pass
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class ToyEnv(gym.Env):
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observation_space = gym.spaces.Box(low=-1, high=1, shape=(1,), dtype=np.float64)
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action_space = gym.spaces.Box(low=-1, high=1, shape=(1,), dtype=np.float64)
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dt = 0.02
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def __init__(self, a: int = 0, b: float = 0.0, c: list = [], d: dict = {}, e: Object = Object()):
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self.a, self.b, self.c, self.d, self.e = a, b, c, d, e
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def reset(self, *, seed: Optional[int] = None, return_info: bool = False,
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options: Optional[dict] = None) -> Union[ObsType, Tuple[ObsType, dict]]:
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obs, options = np.array([-1]), {}
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return obs, options
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def step(self, action: ActType) -> Tuple[ObsType, float, bool, dict]:
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obs, reward, terminated, truncated, info = np.array([-1]), 1, False, False, {}
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return obs, reward, terminated, truncated, info
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def render(self):
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pass
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class ToyWrapper(RawInterfaceWrapper):
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@property
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def current_pos(self) -> Union[float, int, np.ndarray, Tuple]:
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return np.ones(self.action_space.shape)
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@property
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def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
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return np.zeros(self.action_space.shape)
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@pytest.fixture(scope="session", autouse=True)
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def setup():
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register(
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id=f'toy-v0',
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entry_point='test.test_black_box:ToyEnv',
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max_episode_steps=50,
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)
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@pytest.mark.parametrize('env_id', ENV_IDS)
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def test_missing_wrapper(env_id: str):
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with pytest.raises(ValueError):
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fancy_gym.make_bb(env_id, [], {}, {}, {}, {}, {})
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@pytest.mark.parametrize('mp_type', ['promp', 'dmp', 'prodmp'])
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def test_missing_local_state(mp_type: str):
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basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
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env = fancy_gym.make_bb('toy-v0', [RawInterfaceWrapper], {},
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{'trajectory_generator_type': mp_type},
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{'controller_type': 'motor'},
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{'phase_generator_type': 'exp'},
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{'basis_generator_type': basis_generator_type})
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env.reset(seed=SEED)
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with pytest.raises(NotImplementedError):
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env.step(env.action_space.sample())
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@pytest.mark.parametrize('mp_type', ['promp', 'dmp', 'prodmp'])
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@pytest.mark.parametrize('env_wrap', zip(ENV_IDS, WRAPPERS))
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@pytest.mark.parametrize('verbose', [1, 2])
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def test_verbosity(mp_type: str, env_wrap: Tuple[str, Type[RawInterfaceWrapper]], verbose: int):
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basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
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env_id, wrapper_class = env_wrap
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env = fancy_gym.make_bb(env_id, [wrapper_class], {'verbose': verbose},
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{'trajectory_generator_type': mp_type},
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{'controller_type': 'motor'},
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{'phase_generator_type': 'exp'},
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{'basis_generator_type': basis_generator_type})
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env.reset(seed=SEED)
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_obs, _reward, _terminated, _truncated, info = env.step(env.action_space.sample())
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info_keys = list(info.keys())
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env_step = make(env_id)
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env_step.reset()
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_obs, _reward, _terminated, _truncated, info = env_step.step(env_step.action_space.sample())
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info_keys_step = info.keys()
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assert all(e in info_keys for e in info_keys_step)
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assert 'trajectory_length' in info_keys
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if verbose >= 2:
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mp_keys = ['positions', 'velocities', 'step_actions', 'step_observations', 'step_rewards']
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assert all(e in info_keys for e in mp_keys)
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@pytest.mark.parametrize('mp_type', ['promp', 'dmp', 'prodmp'])
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@pytest.mark.parametrize('env_wrap', zip(ENV_IDS, WRAPPERS))
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def test_length(mp_type: str, env_wrap: Tuple[str, Type[RawInterfaceWrapper]]):
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basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
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env_id, wrapper_class = env_wrap
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env = fancy_gym.make_bb(env_id, [wrapper_class], {},
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{'trajectory_generator_type': mp_type},
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{'controller_type': 'motor'},
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{'phase_generator_type': 'exp'},
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{'basis_generator_type': basis_generator_type}, fallback_max_steps=MAX_STEPS_FALLBACK)
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for i in range(5):
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env.reset(seed=SEED)
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_obs, _reward, _terminated, _truncated, info = env.step(env.action_space.sample())
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length = info['trajectory_length']
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assert length == env.spec.max_episode_steps, f'Expcted total simulation length ({length}) to be equal to spec.max_episode_steps ({env.spec.max_episode_steps}), but was not during test nr. {i}'
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@pytest.mark.parametrize('mp_type', ['promp', 'dmp', 'prodmp'])
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@pytest.mark.parametrize('reward_aggregation', [np.sum, np.mean, np.median, lambda x: np.mean(x[::2])])
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def test_aggregation(mp_type: str, reward_aggregation: Callable[[np.ndarray], float]):
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basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
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env = fancy_gym.make_bb('toy-v0', [ToyWrapper], {'reward_aggregation': reward_aggregation},
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{'trajectory_generator_type': mp_type},
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{'controller_type': 'motor'},
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{'phase_generator_type': 'exp'},
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{'basis_generator_type': basis_generator_type})
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env.reset(seed=SEED)
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# ToyEnv only returns 1 as reward
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_obs, reward, _terminated, _truncated, _info = env.step(env.action_space.sample())
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assert reward == reward_aggregation(np.ones(50, ))
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@pytest.mark.parametrize('mp_type', ['promp', 'dmp'])
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@pytest.mark.parametrize('env_wrap', zip(ENV_IDS, WRAPPERS))
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def test_context_space(mp_type: str, env_wrap: Tuple[str, Type[RawInterfaceWrapper]]):
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env_id, wrapper_class = env_wrap
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env = fancy_gym.make_bb(env_id, [wrapper_class], {},
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{'trajectory_generator_type': mp_type},
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{'controller_type': 'motor'},
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{'phase_generator_type': 'exp'},
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{'basis_generator_type': 'rbf'})
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# check if observation space matches with the specified mask values which are true
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env_step = make(env_id)
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wrapper = wrapper_class(env_step)
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assert env.observation_space.shape == wrapper.context_mask[wrapper.context_mask].shape
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@pytest.mark.parametrize('mp_type', ['promp', 'dmp', 'prodmp'])
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@pytest.mark.parametrize('num_dof', [0, 1, 2, 5])
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@pytest.mark.parametrize('num_basis', [
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pytest.param(0, marks=pytest.mark.xfail(reason="Basis Length 0 is not yet implemented.")),
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1, 2, 5])
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@pytest.mark.parametrize('learn_tau', [True, False])
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@pytest.mark.parametrize('learn_delay', [True, False])
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def test_action_space(mp_type: str, num_dof: int, num_basis: int, learn_tau: bool, learn_delay: bool):
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basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
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env = fancy_gym.make_bb('toy-v0', [ToyWrapper], {},
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{'trajectory_generator_type': mp_type,
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'action_dim': num_dof
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},
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{'controller_type': 'motor'},
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{'phase_generator_type': 'exp',
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'learn_tau': learn_tau,
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'learn_delay': learn_delay
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},
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{'basis_generator_type': basis_generator_type,
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'num_basis': num_basis
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})
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base_dims = num_dof * num_basis
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additional_dims = num_dof if 'dmp' in mp_type else 0
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traj_modification_dims = int(learn_tau) + int(learn_delay)
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assert env.action_space.shape[0] == base_dims + traj_modification_dims + additional_dims
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@pytest.mark.parametrize('mp_type', ['promp', 'dmp', 'prodmp'])
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@pytest.mark.parametrize('a', [1])
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@pytest.mark.parametrize('b', [1.0])
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@pytest.mark.parametrize('c', [[1], [1.0], ['str'], [{'a': 'b'}], [np.ones(3, )]])
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@pytest.mark.parametrize('d', [{'a': 1}, {1: 2.0}, {'a': [1.0]}, {'a': np.ones(3, )}, {'a': {'a': 'b'}}])
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@pytest.mark.parametrize('e', [Object()])
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def test_change_env_kwargs(mp_type: str, a: int, b: float, c: list, d: dict, e: Object):
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basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
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env = fancy_gym.make_bb('toy-v0', [ToyWrapper], {},
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{'trajectory_generator_type': mp_type},
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{'controller_type': 'motor'},
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{'phase_generator_type': 'exp'},
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{'basis_generator_type': basis_generator_type},
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a=a, b=b, c=c, d=d, e=e
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)
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assert a is env.a
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assert b is env.b
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assert c is env.c
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# Due to how gym works dict kwargs need to be copied and hence can only be checked to have the same content
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assert d == env.d
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assert e is env.e
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@pytest.mark.parametrize('mp_type', ['promp', 'prodmp'])
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@pytest.mark.parametrize('tau', [0.25, 0.5, 0.75, 1])
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def test_learn_tau(mp_type: str, tau: float):
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phase_generator_type = 'exp' if mp_type == 'prodmp' else 'linear'
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basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
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env = fancy_gym.make_bb('toy-v0', [ToyWrapper], {'verbose': 2},
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{'trajectory_generator_type': mp_type,
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},
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{'controller_type': 'motor'},
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{'phase_generator_type': phase_generator_type,
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'learn_tau': True,
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'learn_delay': False
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},
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{'basis_generator_type': basis_generator_type,
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})
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env.reset(seed=SEED)
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done = True
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for i in range(5):
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if done:
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env.reset(seed=SEED)
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action = env.action_space.sample()
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action[0] = tau
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_obs, _reward, terminated, truncated, info = env.step(action)
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done = terminated or truncated
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length = info['trajectory_length']
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assert length == env.spec.max_episode_steps
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tau_time_steps = int(np.round(tau / env.dt))
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pos = info['positions'].flatten()
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vel = info['velocities'].flatten()
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# Check end is all same (only true for linear basis)
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if phase_generator_type == "linear":
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assert np.all(pos[tau_time_steps:] == pos[-1])
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assert np.all(vel[tau_time_steps:] == vel[-1])
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# Check active trajectory section is different to end values
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assert np.all(pos[:tau_time_steps - 1] != pos[-1])
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assert np.all(vel[:tau_time_steps - 2] != vel[-1])
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#
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#
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@pytest.mark.parametrize('mp_type', ['promp', 'prodmp'])
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@pytest.mark.parametrize('delay', [0, 0.25, 0.5, 0.75])
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def test_learn_delay(mp_type: str, delay: float):
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basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
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phase_generator_type = 'exp' if mp_type == 'prodmp' else 'linear'
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env = fancy_gym.make_bb('toy-v0', [ToyWrapper], {'verbose': 2},
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{'trajectory_generator_type': mp_type,
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},
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{'controller_type': 'motor'},
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{'phase_generator_type': phase_generator_type,
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'learn_tau': False,
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'learn_delay': True
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},
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{'basis_generator_type': basis_generator_type,
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})
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env.reset(seed=SEED)
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done = True
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for i in range(5):
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if done:
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env.reset(seed=SEED)
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action = env.action_space.sample()
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action[0] = delay
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_obs, _reward, terminated, truncated, info = env.step(action)
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done = terminated or truncated
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length = info['trajectory_length']
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assert length == env.spec.max_episode_steps
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delay_time_steps = int(np.round(delay / env.dt))
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pos = info['positions'].flatten()
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vel = info['velocities'].flatten()
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# Check beginning is all same (only true for linear basis)
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assert np.all(pos[:max(1, delay_time_steps - 1)] == pos[0])
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assert np.all(vel[:max(1, delay_time_steps - 2)] == vel[0])
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# Check active trajectory section is different to beginning values
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assert np.all(pos[max(1, delay_time_steps):] != pos[0])
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assert np.all(vel[max(1, delay_time_steps)] != vel[0])
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#
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#
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@pytest.mark.parametrize('mp_type', ['promp', 'prodmp'])
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@pytest.mark.parametrize('tau', [0.25, 0.5, 0.75, 1])
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@pytest.mark.parametrize('delay', [0.25, 0.5, 0.75, 1])
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def test_learn_tau_and_delay(mp_type: str, tau: float, delay: float):
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phase_generator_type = 'exp' if mp_type == 'prodmp' else 'linear'
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basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
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env = fancy_gym.make_bb('toy-v0', [ToyWrapper], {'verbose': 2},
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{'trajectory_generator_type': mp_type,
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},
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{'controller_type': 'motor'},
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{'phase_generator_type': phase_generator_type,
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'learn_tau': True,
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'learn_delay': True
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},
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{'basis_generator_type': basis_generator_type,
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})
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env.reset(seed=SEED)
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if env.spec.max_episode_steps * env.dt < delay + tau:
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return
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done = True
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for i in range(5):
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if done:
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env.reset(seed=SEED)
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action = env.action_space.sample()
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action[0] = tau
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action[1] = delay
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_obs, _reward, terminated, truncated, info = env.step(action)
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done = terminated or truncated
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length = info['trajectory_length']
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assert length == env.spec.max_episode_steps
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tau_time_steps = int(np.round(tau / env.dt))
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delay_time_steps = int(np.round(delay / env.dt))
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joint_time_steps = delay_time_steps + tau_time_steps
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pos = info['positions'].flatten()
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vel = info['velocities'].flatten()
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# Check end is all same (only true for linear basis)
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if phase_generator_type == "linear":
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assert np.all(pos[joint_time_steps:] == pos[-1])
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assert np.all(vel[joint_time_steps:] == vel[-1])
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# Check beginning is all same (only true for linear basis)
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assert np.all(pos[:delay_time_steps - 1] == pos[0])
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assert np.all(vel[:delay_time_steps - 2] == vel[0])
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# Check active trajectory section is different to beginning and end values
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active_pos = pos[delay_time_steps: joint_time_steps - 1]
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active_vel = vel[delay_time_steps: joint_time_steps - 2]
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assert np.all(active_pos != pos[-1]) and np.all(active_pos != pos[0])
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assert np.all(active_vel != vel[-1]) and np.all(active_vel != vel[0])
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