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examples updated

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Fabian 2022-07-12 14:33:20 +02:00
parent 993df10fad
commit 79c26681c9
1 changed files with 4 additions and 5 deletions
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alr_envs/examples/examples_movement_primitives.py
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@ -36,11 +36,11 @@ def example_mp(env_name="HoleReacherProMP-v0", seed=1, iterations=1, render=True
env.render(mode=None) env.render(mode=None)
# Now the action space is not the raw action but the parametrization of the trajectory generator, # Now the action space is not the raw action but the parametrization of the trajectory generator,
# such as a ProMP # such as a ProMP. You can still use it the same, though.
ac = env.action_space.sample() ac = env.action_space.sample()
# This executes a full trajectory # This executes a full trajectory
obs, reward, done, info = env.step(ac) obs, reward, done, info = env.step(ac)
# Aggregated reward # Aggregated reward of trajectory
rewards += reward rewards += reward
if done: if done:
@ -62,9 +62,8 @@ def example_custom_mp(env_name="Reacher5dProMP-v0", seed=1, iterations=1, render
""" """
# Changing the arguments of the black box env is possible by providing them to gym as with all kwargs. # 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 # E.g. here for adding a lot of basis functions
# env = alr_envs.make(env_name, seed, basis_generator_kwargs={'num_basis': 1000}) env = alr_envs.make(env_name, seed, basis_generator_kwargs={'num_basis': 1000})
env = alr_envs.make(env_name, seed)
# mp_dict.update({'black_box_kwargs': {'learn_sub_trajectories': True}}) # 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}}) # mp_dict.update({'black_box_kwargs': {'do_replanning': lambda pos, vel, t: lambda t: t % 100}})