121 lines
3.8 KiB
Python
121 lines
3.8 KiB
Python
from alr_envs import HoleReacherMPWrapper
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from alr_envs.utils.make_env_helpers import make_dmp_env, make_env
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def example_mp(env_name="alr_envs:HoleReacherDMP-v1", seed=1):
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"""
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Example for running a motion primitive based environment, which is already registered
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Args:
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env_name: DMP env_id
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seed: seed
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Returns:
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"""
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# While in this case gym.make() is possible to use as well, we recommend our custom make env function.
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# First, it already takes care of seeding and second enables the use of DMC tasks within the gym interface.
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env = make_env(env_name, seed)
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# Changing the mp_kwargs is possible by providing them to gym.
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# E.g. here by providing way to many basis functions
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# mp_kwargs = {
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# "num_dof": 5,
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# "num_basis": 1000,
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# "duration": 2,
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# "learn_goal": True,
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# "alpha_phase": 2,
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# "bandwidth_factor": 2,
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# "policy_type": "velocity",
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# "weights_scale": 50,
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# "goal_scale": 0.1
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# }
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# env = make_env(env_name, seed, mp_kwargs=mp_kwargs)
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rewards = 0
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# env.render(mode=None)
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obs = env.reset()
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# number of samples/full trajectories (multiple environment steps)
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for i in range(10):
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ac = env.action_space.sample()
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obs, reward, done, info = env.step(ac)
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rewards += reward
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if i % 1 == 0:
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# render full DMP trajectory
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# render can only be called once in the beginning as well. That would render every trajectory
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# Calling it after every trajectory allows to modify the mode. mode=None, disables rendering.
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env.render(mode="human")
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if done:
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print(rewards)
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rewards = 0
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obs = env.reset()
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def example_custom_mp(seed=1):
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"""
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Example for running a custom motion primitive based environments.
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Our already registered environments follow the same structure.
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Hence, this also allows to adjust hyperparameters of the motion primitives.
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Yet, we recommend the method above if you are just interested in chaining those parameters for existing tasks.
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We appreciate PRs for custom environments (especially MP wrappers of existing tasks)
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for our repo: https://github.com/ALRhub/alr_envs/
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Args:
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seed: seed
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Returns:
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"""
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base_env = "alr_envs:HoleReacher-v1"
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# Replace this wrapper with the custom wrapper for your environment by inheriting from the MPEnvWrapper.
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# You can also add other gym.Wrappers in case they are needed.
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wrappers = [HoleReacherMPWrapper]
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mp_kwargs = {
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"num_dof": 5,
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"num_basis": 5,
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"duration": 2,
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"learn_goal": True,
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"alpha_phase": 2,
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"bandwidth_factor": 2,
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"policy_type": "velocity",
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"weights_scale": 50,
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"goal_scale": 0.1
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}
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env = make_dmp_env(base_env, wrappers=wrappers, seed=seed, **mp_kwargs)
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# OR for a deterministic ProMP:
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# env = make_detpmp_env(base_env, wrappers=wrappers, seed=seed)
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rewards = 0
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# env.render(mode=None)
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obs = env.reset()
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# number of samples/full trajectories (multiple environment steps)
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for i in range(10):
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ac = env.action_space.sample()
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obs, reward, done, info = env.step(ac)
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rewards += reward
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if i % 1 == 0:
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# render full DMP trajectory
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# render can only be called once in the beginning as well. That would render every trajectory
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# Calling it after every trajectory allows to modify the mode. mode=None, disables rendering.
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env.render(mode="human")
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if done:
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print(rewards)
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rewards = 0
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obs = env.reset()
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if __name__ == '__main__':
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# DMP
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example_mp("alr_envs:HoleReacherDMP-v1")
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# DetProMP
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example_mp("alr_envs:HoleReacherDetPMP-v1")
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# Custom DMP
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example_custom_mp()
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