55 lines
1.9 KiB
Python
55 lines
1.9 KiB
Python
from alr_envs.utils.dmp_env_wrapper import DmpEnvWrapper
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from alr_envs.utils.dmp_async_vec_env import DmpAsyncVectorEnv, _worker
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from alr_envs.classic_control.hole_reacher import HoleReacher
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import numpy as np
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if __name__ == "__main__":
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def make_env(rank, seed=0):
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"""
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Utility function for multiprocessed env.
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:param env_id: (str) the environment ID
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:param num_env: (int) the number of environments you wish to have in subprocesses
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:param seed: (int) the inital seed for RNG
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:param rank: (int) index of the subprocess
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"""
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def _init():
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_env = HoleReacher(num_links=5,
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allow_self_collision=False,
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allow_wall_collision=False,
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hole_width=0.15,
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hole_depth=1,
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hole_x=1)
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_env = DmpEnvWrapper(_env,
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num_dof=5,
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num_basis=5,
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duration=2,
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dt=_env.dt,
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learn_goal=True,
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alpha_phase=2,
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start_pos=_env.start_pos,
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policy_type="velocity"
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)
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_env.seed(seed + rank)
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return _env
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return _init
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n_samples = 4
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env = DmpAsyncVectorEnv([make_env(i) for i in range(4)],
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n_samples=n_samples,
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context="spawn",
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shared_memory=False,
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worker=_worker)
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# params = np.random.randn(4, 25)
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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])])
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# env.reset()
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out = env(params)
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print(out)
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