dodox/fancy_gym
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Docs: Show usage using new api functionality

Browse Source
This commit is contained in:
Dominik Moritz Roth 2023-10-29 13:25:35 +01:00
parent 668765b145
commit f1d3b5333f
1 changed files with 120 additions and 30 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

150
fancy_gym/examples/examples_movement_primitives.py
View File

@ -45,10 +45,14 @@ def example_mp(env_name="fancy_ProMP/HoleReacher-v0", seed=1, iterations=1, rend
def example_custom_mp(env_name="fancy_ProMP/Reacher5d-v0", seed=1, iterations=1, render=True):
"""
Example for running a movement primitive based environment, which is already registered
Example for running a custom movement primitive based environments.
Our already registered environments follow the same structure.
Hence, this also allows to adjust hyperparameters of the movement primitives.
Yet, we recommend the method above if you are just interested in changing those parameters for existing tasks.
We appreciate PRs for custom environments (especially MP wrappers of existing tasks)
for our repo: https://github.com/ALRhub/fancy_gym/
Args:
env_name: DMP env_id
seed: seed for deterministic behaviour
seed: seed
iterations: Number of rollout steps to run
render: Render the episode
@ -78,6 +82,44 @@ def example_custom_mp(env_name="fancy_ProMP/Reacher5d-v0", seed=1, iterations=1,
return obs
class Custom_MPWrapper(fancy_gym.envs.mujoco.reacher.MPWrapper):
mp_config = {
'ProMP': {
'trajectory_generator_kwargs': {
'trajectory_generator_type': 'promp',
'weights_scale': 2
},
'phase_generator_kwargs': {
'phase_generator_type': 'linear'
},
'controller_kwargs': {
'controller_type': 'velocity'
},
'basis_generator_kwargs': {
'basis_generator_type': 'zero_rbf',
'num_basis': 5,
'num_basis_zero_start': 1
}
},
'DMP': {
'trajectory_generator_kwargs': {
'trajectory_generator_type': 'dmp',
'weights_scale': 500
},
'phase_generator_kwargs': {
'phase_generator_type': 'exp',
'alpha_phase': 2.5
},
'controller_kwargs': {
'controller_type': 'velocity'
},
'basis_generator_kwargs': {
'basis_generator_type': 'rbf',
'num_basis': 5
}
}
}
def example_fully_custom_mp(seed=1, iterations=1, render=True):
"""
@ -97,35 +139,13 @@ def example_fully_custom_mp(seed=1, iterations=1, render=True):
"""
base_env_id = "fancy/Reacher5d-v0"
custom_env_id = "fancy/Reacher5d-Custom-v0"
custom_env_id_DMP = "fancy_DMP/Reacher5d-Custom-v0"
custom_env_id_ProMP = "fancy_ProMP/Reacher5d-Custom-v0"
# Replace this wrapper with the custom wrapper for your environment by inheriting from the RawInterfaceWrapper.
# You can also add other gym.Wrappers in case they are needed.
wrappers = [fancy_gym.envs.mujoco.reacher.MPWrapper]
fancy_gym.upgrade(custom_env_id, mp_wrapper=Custom_MPWrapper, add_mp_types=['ProMP', 'DMP'], base_id=base_env_id)
# For a ProMP
trajectory_generator_kwargs = {'trajectory_generator_type': 'promp',
'weights_scale': 2}
phase_generator_kwargs = {'phase_generator_type': 'linear'}
controller_kwargs = {'controller_type': 'velocity'}
basis_generator_kwargs = {'basis_generator_type': 'zero_rbf',
'num_basis': 5,
'num_basis_zero_start': 1
}
# # For a DMP
# trajectory_generator_kwargs = {'trajectory_generator_type': 'dmp',
# 'weights_scale': 500}
# phase_generator_kwargs = {'phase_generator_type': 'exp',
# 'alpha_phase': 2.5}
# controller_kwargs = {'controller_type': 'velocity'}
# basis_generator_kwargs = {'basis_generator_type': 'rbf',
# 'num_basis': 5
# }
raw_env = gym.make(base_env_id, render_mode='human' if render else None)
env = fancy_gym.make_bb(env=raw_env, wrappers=wrappers, black_box_kwargs={},
traj_gen_kwargs=trajectory_generator_kwargs, controller_kwargs=controller_kwargs,
phase_kwargs=phase_generator_kwargs, basis_kwargs=basis_generator_kwargs,
seed=seed)
env = gym.make(custom_env_id_ProMP, render_mode='human' if render else None)
if render:
env.render()
@ -144,6 +164,75 @@ def example_fully_custom_mp(seed=1, iterations=1, render=True):
rewards = 0
obs = env.reset()
def example_fully_custom_mp_alternative(seed=1, iterations=1, render=True):
"""
Instead of defining the mp_args in a new custom MP_Wrapper, they can also be provided during registration.
Args:
seed: seed
iterations: Number of rollout steps to run
render: Render the episode
Returns:
"""
base_env_id = "fancy/Reacher5d-v0"
custom_env_id = "fancy/Reacher5d-Custom-v0"
custom_env_id_ProMP = "fancy_ProMP/Reacher5d-Custom-v0"
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': {
'trajectory_generator_kwargs': {
'trajectory_generator_type': 'promp',
'weights_scale': 2
},
'phase_generator_kwargs': {
'phase_generator_type': 'linear'
},
'controller_kwargs': {
'controller_type': 'velocity'
},
'basis_generator_kwargs': {
'basis_generator_type': 'zero_rbf',
'num_basis': 5,
'num_basis_zero_start': 1
}
}})
env = gym.make(custom_env_id_ProMP, render_mode='human' if render else None)
if render:
env.render()
rewards = 0
obs = env.reset()
# number of samples/full trajectories (multiple environment steps)
for i in range(iterations):
ac = env.action_space.sample()
obs, reward, terminated, truncated, info = env.step(ac)
rewards += reward
if terminated or truncated:
print(rewards)
rewards = 0
obs = env.reset()
if render:
env.render()
rewards = 0
obs = env.reset()
# number of samples/full trajectories (multiple environment steps)
for i in range(iterations):
ac = env.action_space.sample()
obs, reward, terminated, truncated, info = env.step(ac)
rewards += reward
if terminated or truncated:
print(rewards)
rewards = 0
obs = env.reset()
def main():
render = False
@ -165,6 +254,7 @@ def main():
# Custom MP
example_fully_custom_mp(seed=10, iterations=1, render=render)
example_fully_custom_mp_alternative(seed=10, iterations=1, render=render)
if __name__=='__main__':
main()