dodox/fancy_gym
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

delete hacky experimental codes & add tests to test_black_box

Browse Source
This commit is contained in:
Hongyi Zhou
2022-11-13 16:59:13 +01:00
parent 60e18d2964
commit 104b909296
3 changed files with 86 additions and 33 deletions
Download Patch File Download Diff File Expand all files Collapse all files
fancy_gym/black_box/black_box_wrapper.py
+4 -16
View File
@@ -24,7 +24,7 @@ class BlackBoxWrapper(gym.ObservationWrapper):
Callable[[np.ndarray, np.ndarray, np.ndarray, np.ndarray, int], bool]] = None,
reward_aggregation: Callable[[np.ndarray], float] = np.sum,
max_planning_times: int = 1,
desired_conditioning: bool = False
desired_traj_bc: bool = False
):
"""
gym.Wrapper for leveraging a black box approach with a trajectory generator.
@@ -59,18 +59,11 @@ class BlackBoxWrapper(gym.ObservationWrapper):
# reward computation
self.reward_aggregation = reward_aggregation
# self.traj_gen.basis_gn.show_basis(plot=True)
# spaces
self.return_context_observation = not (learn_sub_trajectories or self.do_replanning)
# self.return_context_observation = True
self.traj_gen_action_space = self._get_traj_gen_action_space()
self.action_space = self._get_action_space()
# no goal learning
# tricky_action_upperbound = [np.inf] * (self.traj_gen_action_space.shape[0] - 7)
# tricky_action_lowerbound = [-np.inf] * (self.traj_gen_action_space.shape[0] - 7)
# self.action_space = spaces.Box(np.array(tricky_action_lowerbound), np.array(tricky_action_upperbound), dtype=np.float32)
self.observation_space = self._get_observation_space()
# rendering
@@ -78,7 +71,7 @@ class BlackBoxWrapper(gym.ObservationWrapper):
self.verbose = verbose
# condition value
self.desired_conditioning = True
self.desired_traj_bc = desired_traj_bc
self.condition_pos = None
self.condition_vel = None
@@ -157,11 +150,6 @@ class BlackBoxWrapper(gym.ObservationWrapper):
def step(self, action: np.ndarray):
""" This function generates a trajectory based on a MP and then does the usual loop over reset and step"""
## tricky part, only use weights basis
# basis_weights = action.reshape(7, -1)
# goal_weights = np.zeros((7, 1))
# action = np.concatenate((basis_weights, goal_weights), axis=1).flatten()
# TODO remove this part, right now only needed for beer pong
mp_params, env_spec_params = self.env.episode_callback(action, self.traj_gen)
position, velocity = self.get_trajectory(mp_params)
@@ -201,8 +189,8 @@ class BlackBoxWrapper(gym.ObservationWrapper):
if self.max_planning_times is not None and self.plan_counts >= self.max_planning_times:
continue
self.condition_pos = pos if self.desired_conditioning else self.current_pos
self.condition_vel = vel if self.desired_conditioning else self.current_vel
self.condition_pos = pos if self.desired_traj_bc else self.current_pos
self.condition_vel = vel if self.desired_traj_bc else self.current_vel
break