todos

alr_envs/alr/__init__.py

@@ -24,13 +24,17 @@ ALL_ALR_MOTION_PRIMITIVE_ENVIRONMENTS = {"DMP": [], "ProMP": []}
 DEFAULT_MP_ENV_DICT = {
     "name": 'EnvName',
     "wrappers": [],
+    # TODO move scale to traj_gen
     "ep_wrapper_kwargs": {
         "weight_scale": 1
     },
+    # TODO traj_gen_kwargs
+    # TODO remove action_dim
     "movement_primitives_kwargs": {
         'movement_primitives_type': 'promp',
         'action_dim': 7
     },
+    # TODO remove tau
     "phase_generator_kwargs": {
         'phase_generator_type': 'linear',
         'delay': 0,
@@ -40,13 +44,13 @@ DEFAULT_MP_ENV_DICT = {
     },
     "controller_kwargs": {
         'controller_type': 'motor',
-        "p_gains": np.ones(7),
+        "p_gains": 1.0,
-        "d_gains": np.ones(7) * 0.1,
+        "d_gains": 0.1,
     },
     "basis_generator_kwargs": {
         'basis_generator_type': 'zero_rbf',
         'num_basis': 5,
-        'num_basis_zero_start': 2
+        'num_basis_zero_start': 2  # TODO: Change to 1
     }
 }
 

alr_envs/alr/mujoco/half_cheetah_jump/new_mp_wrapper.py

@@ -2,8 +2,6 @@ from typing import Tuple, Union
 
 import numpy as np
 
-from mp_env_api import MPEnvWrapper
-
 from alr_envs.mp.raw_interface_wrapper import RawInterfaceWrapper
 
 

alr_envs/mp/black_box_wrapper.py

@@ -32,18 +32,24 @@ class BlackBoxWrapper(gym.ObservationWrapper, ABC):
 
         self.env = env
         self.duration = duration
-        self.traj_steps = int(duration / self.dt)
+        self.sequencing = sequencing
-        self.post_traj_steps = self.env.spec.max_episode_steps - self.traj_steps
+        # 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.time_steps = np.linspace(0, self.duration, self.traj_steps)
-        self.trajectory_generator.set_mp_times(self.time_steps)
+        # self.trajectory_generator.set_mp_times(self.time_steps)
-        # self.trajectory_generator.set_mp_duration(self.time_steps, dt)
+        if not sequencing:
-        # action_bounds = np.inf * np.ones((np.prod(self.trajectory_generator.num_params)))
+            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

@@ -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