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integrated metaworld tasks into the framework

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ottofabian
2021-08-19 09:30:54 +02:00
parent a11965827d
commit 9b1ccb3235
17 changed files with 429 additions and 43 deletions
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alr_envs/examples/examples_dmc.py
+1 -1
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@@ -17,7 +17,7 @@ def example_dmc(env_id="fish-swim", seed=1, iterations=1000, render=True):
Returns:
"""
env = alr_envs.make_env(env_id, seed)
env = alr_envs.make(env_id, seed)
rewards = 0
obs = env.reset()
print("observation shape:", env.observation_space.shape)
alr_envs/examples/examples_general.py
+2 -2
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@@ -21,7 +21,7 @@ def example_general(env_id="Pendulum-v0", seed=1, iterations=1000, render=True):
"""
env = alr_envs.make_env(env_id, seed)
env = alr_envs.make(env_id, seed)
rewards = 0
obs = env.reset()
print("Observation shape: ", env.observation_space.shape)
@@ -56,7 +56,7 @@ def example_async(env_id="alr_envs:HoleReacher-v0", n_cpu=4, seed=int('533D', 16
Returns: Tuple of (obs, reward, done, info) with type np.ndarray
"""
env = gym.vector.AsyncVectorEnv([alr_envs.make_env_rank(env_id, seed, i) for i in range(n_cpu)])
env = gym.vector.AsyncVectorEnv([alr_envs.make_rank(env_id, seed, i) for i in range(n_cpu)])
# OR
# envs = gym.vector.AsyncVectorEnv([make_env(env_id, seed + i) for i in range(n_cpu)])
alr_envs/examples/examples_motion_primitives.py
+5 -6
View File
@@ -1,5 +1,4 @@
from alr_envs import MPWrapper
from alr_envs.utils.make_env_helpers import make_dmp_env, make_env
import alr_envs
def example_mp(env_name="alr_envs:HoleReacherDMP-v1", seed=1, iterations=1, render=True):
@@ -16,7 +15,7 @@ def example_mp(env_name="alr_envs:HoleReacherDMP-v1", seed=1, iterations=1, rend
"""
# While in this case gym.make() is possible to use as well, we recommend our custom make env function.
# First, it already takes care of seeding and second enables the use of DMC tasks within the gym interface.
env = make_env(env_name, seed)
env = alr_envs.make(env_name, seed)
rewards = 0
# env.render(mode=None)
@@ -71,7 +70,7 @@ def example_custom_mp(env_name="alr_envs:HoleReacherDMP-v1", seed=1, iterations=
"weights_scale": 50,
"goal_scale": 0.1
}
env = make_env(env_name, seed, mp_kwargs=mp_kwargs)
env = alr_envs.make(env_name, seed, mp_kwargs=mp_kwargs)
# This time rendering every trajectory
if render:
@@ -113,7 +112,7 @@ def example_fully_custom_mp(seed=1, iterations=1, render=True):
# Replace this wrapper with the custom wrapper for your environment by inheriting from the MPEnvWrapper.
# You can also add other gym.Wrappers in case they are needed.
wrappers = [MPWrapper]
wrappers = [alr_envs.classic_control.hole_reacher.MPWrapper]
mp_kwargs = {
"num_dof": 5,
"num_basis": 5,
@@ -125,7 +124,7 @@ def example_fully_custom_mp(seed=1, iterations=1, render=True):
"weights_scale": 50,
"goal_scale": 0.1
}
env = make_dmp_env(base_env, wrappers=wrappers, seed=seed, mp_kwargs=mp_kwargs)
env = alr_envs.make_dmp_env(base_env, wrappers=wrappers, seed=seed, mp_kwargs=mp_kwargs)
# OR for a deterministic ProMP:
# env = make_detpmp_env(base_env, wrappers=wrappers, seed=seed, mp_kwargs=mp_kwargs)
alr_envs/examples/examples_open_ai.py
+4 -4
View File
@@ -1,4 +1,4 @@
from alr_envs.utils.make_env_helpers import make_env
import alr_envs
def example_mp(env_name, seed=1):
@@ -13,7 +13,7 @@ def example_mp(env_name, seed=1):
"""
# While in this case gym.make() is possible to use as well, we recommend our custom make env function.
env = make_env(env_name, seed)
env = alr_envs.make(env_name, seed)
rewards = 0
obs = env.reset()
@@ -29,13 +29,13 @@ def example_mp(env_name, seed=1):
rewards = 0
obs = env.reset()
if __name__ == '__main__':
# DMP - not supported yet
#example_mp("ReacherDetPMP-v2")
# example_mp("ReacherDMP-v2")
# DetProMP
example_mp("ContinuousMountainCarDetPMP-v0")
example_mp("ReacherDetPMP-v2")
example_mp("FetchReachDenseDetPMP-v1")
example_mp("FetchSlideDenseDetPMP-v1")