82 lines
2.7 KiB
Python
82 lines
2.7 KiB
Python
from itertools import chain
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from typing import Tuple, Type, Union, Optional, Callable
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import gym
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import numpy as np
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import pytest
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from gym import register
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from gym.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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import fancy_gym
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from test.utils import run_env, run_env_determinism
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Fancy_ProDMP_IDS = fancy_gym.ALL_FANCY_MOVEMENT_PRIMITIVE_ENVIRONMENTS['ProDMP']
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All_ProDMP_IDS = fancy_gym.ALL_MOVEMENT_PRIMITIVE_ENVIRONMENTS['ProDMP']
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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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return np.array([-1])
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def step(self, action: ActType) -> Tuple[ObsType, float, bool, dict]:
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return np.array([-1]), 1, False, {}
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def render(self, mode="human"):
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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', All_ProDMP_IDS)
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# def test_replanning_envs(env_id: str):
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# """Tests that ProDMP environments run without errors using random actions."""
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# run_env(env_id)
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#
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# @pytest.mark.parametrize('env_id', All_ProDMP_IDS)
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# def test_replanning_determinism(env_id: str):
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# """Tests that ProDMP environments are deterministic."""
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# run_env_determinism(env_id, 0)
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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()
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with pytest.raises(NotImplementedError):
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env.step(env.action_space.sample()) |