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Onur
2022-05-05 18:50:20 +02:00
parent a26b9f463b
commit 2cc1ab759c
11 changed files with 250 additions and 129 deletions
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alr_envs/mp/episodic_wrapper.py
+10 -5
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@@ -50,13 +50,10 @@ class EpisodicWrapper(gym.Env, ABC):
# rendering
self.render_mode = render_mode
self.render_kwargs = {}
# self.time_steps = np.linspace(0, self.duration, self.traj_steps + 1)
self.time_steps = np.linspace(0, self.duration, self.traj_steps)
self.mp.set_mp_times(self.time_steps)
# action_bounds = np.inf * np.ones((np.prod(self.mp.num_params)))
min_action_bounds, max_action_bounds = mp.get_param_bounds()
self.mp_action_space = gym.spaces.Box(low=min_action_bounds.numpy(), high=max_action_bounds.numpy(),
dtype=np.float32)
self.mp_action_space = self.set_mp_action_space()
self.action_space = self.set_action_space()
self.active_obs = self.set_active_obs()
@@ -65,7 +62,7 @@ class EpisodicWrapper(gym.Env, ABC):
dtype=self.env.observation_space.dtype)
def get_trajectory(self, action: np.ndarray) -> Tuple:
# TODO: this follows the implementation of the mp_pytorch library which includes the paramters tau and delay at
# 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.mp.learn_tau) + int(self.mp.learn_delay)
scaled_mp_params = action.copy()
@@ -84,6 +81,13 @@ class EpisodicWrapper(gym.Env, ABC):
return trajectory, velocity
def set_mp_action_space(self):
"""This function can be used to set up an individual space for the parameters of the mp."""
min_action_bounds, max_action_bounds = self.mp.get_param_bounds()
mp_action_space = gym.spaces.Box(low=min_action_bounds.numpy(), high=max_action_bounds.numpy(),
dtype=np.float32)
return mp_action_space
def set_action_space(self):
"""
This function can be used to modify the action space for considering actions which are not learned via motion
@@ -179,6 +183,7 @@ class EpisodicWrapper(gym.Env, ABC):
step_action = self.controller.get_action(pos_vel[0], pos_vel[1], self.current_pos, self.current_vel)
step_action = self._step_callback(t, env_spec_params, step_action) # include possible callback info
c_action = np.clip(step_action, self.env.action_space.low, self.env.action_space.high)
# print('step/clipped action ratio: ', step_action/c_action)
obs, c_reward, done, info = self.env.step(c_action)
if self.verbose >= 2:
actions[t, :] = c_action