Merge pull request #85 from ALRhub/81_fix_docs_mp
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Hongyi Zhou
GitHub
commit
f6b56d5b0f
@ -16,7 +16,7 @@ def example_mp(env_name="fancy_ProMP/HoleReacher-v0", seed=1, iterations=1, rend
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"""
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# Equivalent to gym, we have a make function which can be used to create environments.
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# It takes care of seeding and enables the use of a variety of external environments using the gym interface.
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env = gym.make(env_name)
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env = gym.make(env_name, render_mode='human' if render else None)
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returns = 0
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# env.render(mode=None)
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@ -26,14 +26,6 @@ def example_mp(env_name="fancy_ProMP/HoleReacher-v0", seed=1, iterations=1, rend
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for i in range(iterations):
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if render and i % 1 == 0:
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# This renders the full MP trajectory
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# It is only required to call render() once in the beginning, which renders every consecutive trajectory.
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# Resetting to no rendering, can be achieved by render(mode=None).
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# It is also possible to change the mode multiple times when
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# e.g. only every second trajectory should be displayed, such as here
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# Just make sure the correct mode is set before executing the step.
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env.render(mode="human")
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else:
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env.render()
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# Now the action space is not the raw action but the parametrization of the trajectory generator,
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@ -49,14 +41,19 @@ def example_mp(env_name="fancy_ProMP/HoleReacher-v0", seed=1, iterations=1, rend
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if terminated or truncated:
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print(reward)
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obs = env.reset()
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env.close()
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def example_custom_mp(env_name="fancy_ProMP/Reacher5d-v0", seed=1, iterations=1, render=True):
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"""
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Example for running a movement primitive based environment, which is already registered
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Example for running a custom movement 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 movement primitives.
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Yet, we recommend the method above if you are just interested in changing 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/fancy_gym/
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Args:
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env_name: DMP env_id
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seed: seed for deterministic behaviour
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seed: seed
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iterations: Number of rollout steps to run
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render: Render the episode
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@ -65,14 +62,14 @@ def example_custom_mp(env_name="fancy_ProMP/Reacher5d-v0", seed=1, iterations=1,
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"""
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# Changing the arguments of the black box env is possible by providing them to gym through mp_config_override.
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# E.g. here for way to many basis functions
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env = gym.make(env_name, seed, mp_config_override={'basis_generator_kwargs': {'num_basis': 1000}})
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env = gym.make(env_name, seed, mp_config_override={'basis_generator_kwargs': {'num_basis': 1000}}, render_mode='human' if render else None)
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returns = 0
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obs = env.reset()
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# This time rendering every trajectory
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if render:
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env.render(mode="human")
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env.render()
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# number of samples/full trajectories (multiple environment steps)
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for i in range(iterations):
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@ -84,8 +81,47 @@ def example_custom_mp(env_name="fancy_ProMP/Reacher5d-v0", seed=1, iterations=1,
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print(i, reward)
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obs = env.reset()
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env.close()
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return obs
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class Custom_MPWrapper(fancy_gym.envs.mujoco.reacher.MPWrapper):
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mp_config = {
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'ProMP': {
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'trajectory_generator_kwargs': {
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'trajectory_generator_type': 'promp',
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'weights_scale': 2
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},
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'phase_generator_kwargs': {
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'phase_generator_type': 'linear'
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},
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'controller_kwargs': {
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'controller_type': 'velocity'
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},
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'basis_generator_kwargs': {
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'basis_generator_type': 'zero_rbf',
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'num_basis': 5,
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'num_basis_zero_start': 1
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}
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},
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'DMP': {
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'trajectory_generator_kwargs': {
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'trajectory_generator_type': 'dmp',
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'weights_scale': 500
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},
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'phase_generator_kwargs': {
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'phase_generator_type': 'exp',
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'alpha_phase': 2.5
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},
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'controller_kwargs': {
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'controller_type': 'velocity'
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},
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'basis_generator_kwargs': {
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'basis_generator_type': 'rbf',
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'num_basis': 5
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}
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}
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}
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def example_fully_custom_mp(seed=1, iterations=1, render=True):
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"""
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@ -105,37 +141,92 @@ def example_fully_custom_mp(seed=1, iterations=1, render=True):
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"""
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base_env_id = "fancy/Reacher5d-v0"
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custom_env_id = "fancy/Reacher5d-Custom-v0"
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custom_env_id_DMP = "fancy_DMP/Reacher5d-Custom-v0"
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custom_env_id_ProMP = "fancy_ProMP/Reacher5d-Custom-v0"
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# Replace this wrapper with the custom wrapper for your environment by inheriting from the RawInterfaceWrapper.
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# You can also add other gym.Wrappers in case they are needed.
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wrappers = [fancy_gym.envs.mujoco.reacher.MPWrapper]
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fancy_gym.upgrade(custom_env_id, mp_wrapper=Custom_MPWrapper, add_mp_types=['ProMP', 'DMP'], base_id=base_env_id)
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# For a ProMP
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trajectory_generator_kwargs = {'trajectory_generator_type': 'promp',
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'weights_scale': 2}
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phase_generator_kwargs = {'phase_generator_type': 'linear'}
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controller_kwargs = {'controller_type': 'velocity'}
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basis_generator_kwargs = {'basis_generator_type': 'zero_rbf',
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env = gym.make(custom_env_id_ProMP, render_mode='human' if render else None)
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rewards = 0
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obs = env.reset()
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if render:
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env.render()
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# number of samples/full trajectories (multiple environment steps)
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for i in range(iterations):
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ac = env.action_space.sample()
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obs, reward, terminated, truncated, info = env.step(ac)
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rewards += reward
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if terminated or truncated:
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print(rewards)
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rewards = 0
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obs = env.reset()
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try: # Some mujoco-based envs don't correlcty implement .close
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env.close()
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except:
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pass
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def example_fully_custom_mp_alternative(seed=1, iterations=1, render=True):
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"""
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Instead of defining the mp_args in a new custom MP_Wrapper, they can also be provided during registration.
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Args:
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seed: seed
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iterations: Number of rollout steps to run
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render: Render the episode
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Returns:
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"""
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base_env_id = "fancy/Reacher5d-v0"
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custom_env_id = "fancy/Reacher5d-Custom-v0"
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custom_env_id_ProMP = "fancy_ProMP/Reacher5d-Custom-v0"
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fancy_gym.upgrade(custom_env_id, mp_wrapper=fancy_gym.envs.mujoco.reacher.MPWrapper, add_mp_types=['ProMP'], base_id=base_env_id, mp_config_override= {'ProMP': {
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'trajectory_generator_kwargs': {
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'trajectory_generator_type': 'promp',
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'weights_scale': 2
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},
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'phase_generator_kwargs': {
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'phase_generator_type': 'linear'
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},
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'controller_kwargs': {
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'controller_type': 'velocity'
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},
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'basis_generator_kwargs': {
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'basis_generator_type': 'zero_rbf',
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'num_basis': 5,
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'num_basis_zero_start': 1
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}
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}})
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# # For a DMP
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# trajectory_generator_kwargs = {'trajectory_generator_type': 'dmp',
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# 'weights_scale': 500}
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# phase_generator_kwargs = {'phase_generator_type': 'exp',
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# 'alpha_phase': 2.5}
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# controller_kwargs = {'controller_type': 'velocity'}
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# basis_generator_kwargs = {'basis_generator_type': 'rbf',
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# 'num_basis': 5
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# }
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env = fancy_gym.make_bb(env_id=base_env_id, wrappers=wrappers, black_box_kwargs={},
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traj_gen_kwargs=trajectory_generator_kwargs, controller_kwargs=controller_kwargs,
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phase_kwargs=phase_generator_kwargs, basis_kwargs=basis_generator_kwargs,
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seed=seed)
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env = gym.make(custom_env_id_ProMP, render_mode='human' if render else None)
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rewards = 0
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obs = env.reset()
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if render:
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env.render(mode="human")
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env.render()
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# number of samples/full trajectories (multiple environment steps)
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for i in range(iterations):
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ac = env.action_space.sample()
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obs, reward, terminated, truncated, info = env.step(ac)
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rewards += reward
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if terminated or truncated:
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print(rewards)
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rewards = 0
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obs = env.reset()
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if render:
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env.render()
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rewards = 0
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obs = env.reset()
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@ -151,8 +242,13 @@ def example_fully_custom_mp(seed=1, iterations=1, render=True):
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rewards = 0
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obs = env.reset()
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try: # Some mujoco-based envs don't correlcty implement .close
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env.close()
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except:
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pass
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if __name__ == '__main__':
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def main():
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render = False
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# DMP
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example_mp("fancy_DMP/HoleReacher-v0", seed=10, iterations=5, render=render)
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@ -172,3 +268,7 @@ if __name__ == '__main__':
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# Custom MP
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example_fully_custom_mp(seed=10, iterations=1, render=render)
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example_fully_custom_mp_alternative(seed=10, iterations=1, render=render)
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if __name__=='__main__':
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main()
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