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This commit is contained in:
Fabian
2022-06-29 12:25:40 +02:00
parent 6e06e11cfa
commit 9b48fc9d48
4 changed files with 25 additions and 20 deletions
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alr_envs/mp/black_box_wrapper.py
+17 -14
View File
@@ -32,18 +32,24 @@ class BlackBoxWrapper(gym.ObservationWrapper, ABC):
self.env = env
self.duration = duration
self.traj_steps = int(duration / self.dt)
self.post_traj_steps = self.env.spec.max_episode_steps - self.traj_steps
self.sequencing = sequencing
# self.traj_steps = int(duration / self.dt)
# self.post_traj_steps = self.env.spec.max_episode_steps - self.traj_steps
# duration = self.env.max_episode_steps * self.dt
# trajectory generation
self.trajectory_generator = trajectory_generator
self.tracking_controller = tracking_controller
# self.weight_scale = weight_scale
self.time_steps = np.linspace(0, self.duration, self.traj_steps)
self.trajectory_generator.set_mp_times(self.time_steps)
# self.trajectory_generator.set_mp_duration(self.time_steps, dt)
# action_bounds = np.inf * np.ones((np.prod(self.trajectory_generator.num_params)))
# self.time_steps = np.linspace(0, self.duration, self.traj_steps)
# self.trajectory_generator.set_mp_times(self.time_steps)
if not sequencing:
self.trajectory_generator.set_mp_duration(np.array([self.duration]), np.array([self.dt]))
else:
# sequencing stuff
pass
# reward computation
self.reward_aggregation = reward_aggregation
# spaces
@@ -67,15 +73,12 @@ class BlackBoxWrapper(gym.ObservationWrapper, ABC):
return observation[self.env.context_mask]
def get_trajectory(self, action: np.ndarray) -> Tuple:
# TODO: this follows the implementation of the mp_pytorch library which includes the parameters tau and delay at
# the beginning of the array.
# ignore_indices = int(self.trajectory_generator.learn_tau) + int(self.trajectory_generator.learn_delay)
# scaled_mp_params = action.copy()
# scaled_mp_params[ignore_indices:] *= self.weight_scale
clipped_params = np.clip(action, self.mp_action_space.low, self.mp_action_space.high)
self.trajectory_generator.set_params(clipped_params)
self.trajectory_generator.set_boundary_conditions(bc_time=self.time_steps[:1], bc_pos=self.current_pos,
# if self.trajectory_generator.learn_tau:
# self.trajectory_generator.set_mp_duration(self.trajectory_generator.tau, np.array([self.dt]))
self.trajectory_generator.set_mp_duration(None if self.sequencing else self.duration, np.array([self.dt]))
self.trajectory_generator.set_boundary_conditions(bc_time=, bc_pos=self.current_pos,
bc_vel=self.current_vel)
traj_dict = self.trajectory_generator.get_mp_trajs(get_pos=True, get_vel=True)
trajectory_tensor, velocity_tensor = traj_dict['pos'], traj_dict['vel']
@@ -152,7 +155,7 @@ class BlackBoxWrapper(gym.ObservationWrapper, ABC):
if self.render_mode is not None:
self.render(mode=self.render_mode, **self.render_kwargs)
if done or self.env.do_replanning(self.env.current_pos, self.env.current_vel, obs, c_action, t):
if done or self.env.do_replanning(self.current_pos, self.current_vel, obs, c_action, t + past_steps):
break
infos.update({k: v[:t + 1] for k, v in infos.items()})
alr_envs/mp/raw_interface_wrapper.py
+1 -1
View File
@@ -43,13 +43,13 @@ class RawInterfaceWrapper(gym.Wrapper):
raise NotImplementedError()
@property
@abstractmethod
def dt(self) -> float:
"""
Control frequency of the environment
Returns: float
"""
return self.env.dt
def do_replanning(self, pos, vel, s, a, t):
# return t % 100 == 0