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learn goal with auto scale basis

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Hongyi Zhou 2022-10-31 22:37:13 +01:00
parent 83eb8f7f64
commit 4f9b1fad25
2 changed files with 10 additions and 8 deletions
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16
fancy_gym/black_box/black_box_wrapper.py
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@ -64,11 +64,13 @@ class BlackBoxWrapper(gym.ObservationWrapper):
# self.return_context_observation = not (learn_sub_trajectories or self.do_replanning) # self.return_context_observation = not (learn_sub_trajectories or self.do_replanning)
self.return_context_observation = True self.return_context_observation = True
self.traj_gen_action_space = self._get_traj_gen_action_space() self.traj_gen_action_space = self._get_traj_gen_action_space()
# self.action_space = self._get_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)
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() self.observation_space = self._get_observation_space()
# rendering # rendering
@ -156,9 +158,9 @@ class BlackBoxWrapper(gym.ObservationWrapper):
""" This function generates a trajectory based on a MP and then does the usual loop over reset and step""" """ 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 ## tricky part, only use weights basis
basis_weights = action.reshape(7, -1) # basis_weights = action.reshape(7, -1)
goal_weights = np.zeros((7, 1)) # goal_weights = np.zeros((7, 1))
action = np.concatenate((basis_weights, goal_weights), axis=1).flatten() # action = np.concatenate((basis_weights, goal_weights), axis=1).flatten()
# TODO remove this part, right now only needed for beer pong # TODO remove this part, right now only needed for beer pong
mp_params, env_spec_params = self.env.episode_callback(action, self.traj_gen) mp_params, env_spec_params = self.env.episode_callback(action, self.traj_gen)

2
fancy_gym/envs/__init__.py
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@ -502,7 +502,7 @@ for _v in _versions:
kwargs_dict_box_pushing_prodmp['trajectory_generator_kwargs']['goal_scale'] = 0.3 kwargs_dict_box_pushing_prodmp['trajectory_generator_kwargs']['goal_scale'] = 0.3
kwargs_dict_box_pushing_prodmp['trajectory_generator_kwargs']['auto_scale_basis'] = True kwargs_dict_box_pushing_prodmp['trajectory_generator_kwargs']['auto_scale_basis'] = True
kwargs_dict_box_pushing_prodmp['trajectory_generator_kwargs']['goal_offset'] = 1.0 kwargs_dict_box_pushing_prodmp['trajectory_generator_kwargs']['goal_offset'] = 1.0
kwargs_dict_box_pushing_prodmp['basis_generator_kwargs']['num_basis'] = 5 kwargs_dict_box_pushing_prodmp['basis_generator_kwargs']['num_basis'] = 4
kwargs_dict_box_pushing_prodmp['basis_generator_kwargs']['alpha'] = 10. kwargs_dict_box_pushing_prodmp['basis_generator_kwargs']['alpha'] = 10.
kwargs_dict_box_pushing_prodmp['basis_generator_kwargs']['basis_bandwidth_factor'] = 3 # 3.5, 4 to try kwargs_dict_box_pushing_prodmp['basis_generator_kwargs']['basis_bandwidth_factor'] = 3 # 3.5, 4 to try
kwargs_dict_box_pushing_prodmp['phase_generator_kwargs']['alpha_phase'] = 3 kwargs_dict_box_pushing_prodmp['phase_generator_kwargs']['alpha_phase'] = 3