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minor bug fixes

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This commit is contained in:
Fabian 2022-07-14 17:43:27 +02:00
parent 84386fd8e4
commit e311ee137a
3 changed files with 8 additions and 6 deletions
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8
fancy_gym/black_box/black_box_wrapper.py
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@ -50,7 +50,7 @@ class BlackBoxWrapper(gym.ObservationWrapper):
self.tracking_controller = tracking_controller self.tracking_controller = tracking_controller
# self.time_steps = np.linspace(0, self.duration, self.traj_steps) # self.time_steps = np.linspace(0, self.duration, self.traj_steps)
# self.traj_gen.set_mp_times(self.time_steps) # self.traj_gen.set_mp_times(self.time_steps)
self.traj_gen.set_duration(np.array([self.duration]), np.array([self.dt])) self.traj_gen.set_duration(self.duration - self.dt, self.dt)
# reward computation # reward computation
self.reward_aggregation = reward_aggregation self.reward_aggregation = reward_aggregation
@ -78,8 +78,8 @@ class BlackBoxWrapper(gym.ObservationWrapper):
self.traj_gen.set_boundary_conditions( self.traj_gen.set_boundary_conditions(
bc_time=np.array(0) if not self.do_replanning else np.array([self.current_traj_steps * self.dt]), bc_time=np.array(0) if not self.do_replanning else np.array([self.current_traj_steps * self.dt]),
bc_pos=self.current_pos, bc_vel=self.current_vel) bc_pos=self.current_pos, bc_vel=self.current_vel)
self.traj_gen.set_duration(None if self.learn_sub_trajectories else np.array([self.duration]), # TODO remove the - self.dt after Bruces fix.
np.array([self.dt])) self.traj_gen.set_duration(None if self.learn_sub_trajectories else self.duration - self.dt, self.dt)
traj_dict = self.traj_gen.get_trajs(get_pos=True, get_vel=True) traj_dict = self.traj_gen.get_trajs(get_pos=True, get_vel=True)
trajectory_tensor, velocity_tensor = traj_dict['pos'], traj_dict['vel'] trajectory_tensor, velocity_tensor = traj_dict['pos'], traj_dict['vel']
@ -87,7 +87,7 @@ class BlackBoxWrapper(gym.ObservationWrapper):
def _get_traj_gen_action_space(self): def _get_traj_gen_action_space(self):
"""This function can be used to set up an individual space for the parameters of the traj_gen.""" """This function can be used to set up an individual space for the parameters of the traj_gen."""
min_action_bounds, max_action_bounds = self.traj_gen.get_param_bounds() min_action_bounds, max_action_bounds = self.traj_gen.get_params_bounds().t()
action_space = gym.spaces.Box(low=min_action_bounds.numpy(), high=max_action_bounds.numpy(), action_space = gym.spaces.Box(low=min_action_bounds.numpy(), high=max_action_bounds.numpy(),
dtype=self.env.action_space.dtype) dtype=self.env.action_space.dtype)
return action_space return action_space

4
fancy_gym/black_box/factory/trajectory_generator_factory.py
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@ -12,7 +12,9 @@ def get_trajectory_generator(
return ProMP(basis_generator, action_dim, **kwargs) return ProMP(basis_generator, action_dim, **kwargs)
elif trajectory_generator_type == "dmp": elif trajectory_generator_type == "dmp":
return DMP(basis_generator, action_dim, **kwargs) return DMP(basis_generator, action_dim, **kwargs)
elif trajectory_generator_type == 'idmp': elif trajectory_generator_type == 'prodmp':
from mp_pytorch.basis_gn import ProDMPBasisGenerator
assert isinstance(basis_generator, ProDMPBasisGenerator)
return ProDMP(basis_generator, action_dim, **kwargs) return ProDMP(basis_generator, action_dim, **kwargs)
else: else:
raise ValueError(f"Specified movement primitive type {trajectory_generator_type} not supported, " raise ValueError(f"Specified movement primitive type {trajectory_generator_type} not supported, "

2
fancy_gym/envs/__init__.py
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@ -126,7 +126,7 @@ for _dims in [5, 7]:
register( register(
id=f'Reacher{_dims}dSparse-v0', id=f'Reacher{_dims}dSparse-v0',
entry_point='fancy_gym.envs.mujoco:ReacherEnv', entry_point='fancy_gym.envs.mujoco:ReacherEnv',
max_episode_steps=MAX_EPISODE_STEPS_REACHER, max_episode_steps=5,
kwargs={ kwargs={
"sparse": True, "sparse": True,
'reward_weight': 200, 'reward_weight': 200,