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mp wrapper fixes

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This commit is contained in:
Fabian
2022-07-06 09:05:35 +02:00
parent eddef33d9a
commit 6704c9d63a
43 changed files with 302 additions and 608 deletions
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alr_envs/examples/examples_general.py
+2 -2
View File
@@ -87,10 +87,10 @@ if __name__ == '__main__':
render = True
# Basic gym task
example_general("Pendulum-v0", seed=10, iterations=200, render=render)
example_general("Reacher5d-v0", seed=10, iterations=200, render=render)
# # Basis task from framework
example_general("alr_envs:HoleReacher-v0", seed=10, iterations=200, render=render)
example_general("Reacher-v0", seed=10, iterations=200, render=render)
# # OpenAI Mujoco task
example_general("HalfCheetah-v2", seed=10, render=render)
alr_envs/examples/examples_movement_primitives.py
+7 -26
View File
@@ -45,7 +45,7 @@ def example_mp(env_name="alr_envs:HoleReacherDMP-v1", seed=1, iterations=1, rend
obs = env.reset()
def example_custom_mp(env_name="alr_envs:HoleReacherDMP-v1", seed=1, iterations=1, render=True):
def example_custom_mp(env_name="Reacher5dProMP-v0", seed=1, iterations=1, render=True):
"""
Example for running a motion primitive based environment, which is already registered
Args:
@@ -57,32 +57,12 @@ def example_custom_mp(env_name="alr_envs:HoleReacherDMP-v1", seed=1, iterations=
Returns:
"""
# Changing the traj_gen_kwargs is possible by providing them to gym.
# E.g. here by providing way to many basis functions
# mp_dict = alr_envs.from_default_config('ALRReacher-v0', {'basis_generator_kwargs': {'num_basis': 10}})
# mp_dict.update({'basis_generator_kwargs': {'num_basis': 10}})
# Changing the arguments of the black box env is possible by providing them to gym as with all kwargs.
# E.g. here for way to many basis functions
env = alr_envs.make(env_name, seed, basis_generator_kwargs={'num_basis': 1000})
# mp_dict.update({'black_box_kwargs': {'learn_sub_trajectories': True}})
# mp_dict.update({'black_box_kwargs': {'do_replanning': lambda pos, vel, t: lambda t: t % 100}})
# default env with promp and no learn_sub_trajectories and replanning
# env = alr_envs.make('ALRReacherProMP-v0', 1, n_links=7)
env = alr_envs.make('ALRReacherProMP-v0', 1, basis_generator_kwargs={'num_basis': 10}, n_links=7)
# env = alr_envs.make('ALRReacher-v0', seed=1, bb_kwargs=mp_dict, n_links=1)
# env = alr_envs.make_bb('ALRReacher-v0', **mp_dict)
mp_kwargs = {
"num_dof": 5,
"num_basis": 1000,
"duration": 2,
"learn_goal": True,
"alpha_phase": 2,
"bandwidth_factor": 2,
"policy_type": "velocity",
"weights_scale": 50,
"goal_scale": 0.1
}
env = alr_envs.make(env_name, seed, mp_kwargs=mp_kwargs)
# This time rendering every trajectory
if render:
env.render(mode="human")
@@ -100,6 +80,7 @@ def example_custom_mp(env_name="alr_envs:HoleReacherDMP-v1", seed=1, iterations=
print(rewards)
rewards = 0
obs = env.reset()
print(obs)
def example_fully_custom_mp(seed=1, iterations=1, render=True):
@@ -169,7 +150,7 @@ if __name__ == '__main__':
# example_mp("alr_envs:HoleReacherDetPMP-v1", seed=10, iterations=1, render=render)
# Altered basis functions
example_custom_mp("alr_envs:HoleReacherDMP-v1", seed=10, iterations=1, render=render)
example_custom_mp("Reacher5dProMP-v0", seed=10, iterations=10, render=render)
# Custom MP
example_fully_custom_mp(seed=10, iterations=1, render=render)
# example_fully_custom_mp(seed=10, iterations=1, render=render)