finalized examples and added seed control

alr_envs/examples/examples_dmc.py

@@ -1,14 +1,15 @@
-from alr_envs.dmc.Ball_in_the_cup_mp_wrapper import BallInCupMPWrapper
+from alr_envs.dmc.Ball_in_the_cup_mp_wrapper import DMCBallInCupMPWrapper
 from alr_envs.utils.make_env_helpers import make_dmp_env, make_env
 
 
-def example_dmc(env_name="fish-swim", seed=1):
+def example_dmc(env_name="fish-swim", seed=1, iterations=1000):
     env = make_env(env_name, seed)
     rewards = 0
     obs = env.reset()
+    print(obs)
 
-    # number of samples/full trajectories (multiple environment steps)
-    for i in range(2000):
+    # number of samples(multiple environment steps)
+    for i in range(10):
         ac = env.action_space.sample()
         obs, reward, done, info = env.step(ac)
         rewards += reward
@@ -37,7 +38,7 @@ def example_custom_dmc_and_mp(seed=1):
     # 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 = [HoleReacherMPWrapper]
-    wrappers = [BallInCupMPWrapper]
+    wrappers = [DMCBallInCupMPWrapper]
     mp_kwargs = {
         "num_dof": 2,  # env.start_pos
         "num_basis": 5,
@@ -69,5 +70,14 @@ def example_custom_dmc_and_mp(seed=1):
 
 
 if __name__ == '__main__':
-    example_dmc()
+    # Disclaimer: DMC environments require the seed to be specified in the beginning.
+    # Adjusting it afterwards with env.seed() is not recommended as it does not affect the underlying physics.
+
+    # Standard DMC task
+    example_dmc("fish_swim", seed=10, iterations=1000)
+
+    # Gym + DMC hybrid task provided in the MP framework
+    example_dmc("dmc_ball_in_cup_dmp-v0", seed=10, iterations=10)
+
+    # Custom DMC task
     example_custom_dmc_and_mp()

alr_envs/examples/examples_motion_primitives.py

@@ -15,6 +15,22 @@ def example_mp(env_name="alr_envs:HoleReacherDMP-v1", seed=1):
     # 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)
+
+    # Changing the mp_kwargs is possible by providing them to gym.
+    # E.g. here by providing way to many basis functions
+    # 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 = make_env(env_name, seed, mp_kwargs=mp_kwargs)
+
     rewards = 0
     # env.render(mode=None)
     obs = env.reset()
@@ -40,8 +56,9 @@ def example_mp(env_name="alr_envs:HoleReacherDMP-v1", seed=1):
 def example_custom_mp(seed=1):
     """
     Example for running a custom motion primitive based environments.
-    Our already registered environments follow the same structure, but do not directly allow for modifications.
-    Hence, this also allows to adjust hyperparameters of the motion primitives more easily.
+    Our already registered environments follow the same structure.
+    Hence, this also allows to adjust hyperparameters of the motion primitives.
+    Yet, we recommend the method above if you are just interested in chaining those parameters for existing tasks.
     We appreciate PRs for custom environments (especially MP wrappers of existing tasks) 
     for our repo: https://github.com/ALRhub/alr_envs/
     Args: