fancy_gym/dmp_env_wrapper_example.py

from alr_envs.classic_control.utils import make_viapointreacher_env
from alr_envs.classic_control.utils import make_holereacher_env, make_holereacher_fix_goal_env
from alr_envs.utils.dmp_async_vec_env import DmpAsyncVectorEnv
import numpy as np


if __name__ == "__main__":

    n_samples = 1
    n_cpus = 4
    dim = 30

    # env = DmpAsyncVectorEnv([make_viapointreacher_env(i) for i in range(n_cpus)],
    #                         n_samples=n_samples)

    test_env = make_holereacher_env(0)()

    # params = np.random.randn(n_samples, dim)
    params = np.array([[ 1.386102  , -3.29980525,  4.70402733,  1.3966668 ,  0.73774902,
         3.14676681, -4.98644416,  6.20303193,  1.30502127, -0.09330522,
         7.62656797, -5.76893033,  3.4706711 , -0.6944142 , -3.33442788,
        12.31421548, -0.72760271, -6.9090723 ,  7.02903814, -8.7236836 ,
         1.4805914 ,  0.53185824, -5.46626893,  0.69692163, 13.58472666,
         0.77199316,  2.02906724, -3.0203244 , -1.00533159, -0.57417351]])

    # params = np.hstack([50 * np.random.randn(n_samples, 25), np.tile(np.array([np.pi/2, -np.pi/4, -np.pi/4, -np.pi/4, -np.pi/4]), [n_samples, 1])])

    rew, info = test_env.rollout(params, render=True)
    print(rew)

    # out = env(params)
    # print(out)