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small bp and tt updates

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This commit is contained in:
Maximilian Huettenrauch
2021-12-07 14:46:31 +01:00
parent a0af743585
commit 92d05a9dfd
5 changed files with 55 additions and 42 deletions
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alr_envs/alr/mujoco/table_tennis/tt_gym.py
+9 -5
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@@ -10,7 +10,7 @@ from alr_envs.alr.mujoco.table_tennis.tt_reward import TT_Reward
#TODO: Check for simulation stability. Make sure the code runs even for sim crash
MAX_EPISODE_STEPS = 2875
MAX_EPISODE_STEPS = 1750
BALL_NAME_CONTACT = "target_ball_contact"
BALL_NAME = "target_ball"
TABLE_NAME = 'table_tennis_table'
@@ -42,9 +42,10 @@ class TTEnvGym(MujocoEnv, utils.EzPickle):
else:
raise ValueError("either 2 or 4 dimensional Contexts available")
action_space_low = np.array([-2.6, -2.0, -2.8, -0.9, -4.8, -1.6, -2.2])
action_space_high = np.array([2.6, 2.0, 2.8, 3.1, 1.3, 1.6, 2.2])
self.action_space = spaces.Box(low=action_space_low, high=action_space_high, dtype='float64')
# has no effect as it is overwritten in init of super
# action_space_low = np.array([-2.6, -2.0, -2.8, -0.9, -4.8, -1.6, -2.2])
# action_space_high = np.array([2.6, 2.0, 2.8, 3.1, 1.3, 1.6, 2.2])
# self.action_space = spaces.Box(low=action_space_low, high=action_space_high, dtype='float64')
self.time_steps = 0
self.init_qpos_tt = np.array([0, 0, 0, 1.5, 0, 0, 1.5, 0, 0, 0])
@@ -159,7 +160,10 @@ class TTEnvGym(MujocoEnv, utils.EzPickle):
done = True
reward = -25
ob = self._get_obs()
return ob, reward, done, {"hit_ball": self.hit_ball} # might add some information here ....
info = {"hit_ball": self.hit_ball,
"q_pos": np.copy(self.sim.data.qpos[:7]),
"ball_pos": np.copy(self.sim.data.qpos[7:])}
return ob, reward, done, info # might add some information here ....
def set_context(self, context):
old_state = self.sim.get_state()
alr_envs/alr/mujoco/table_tennis/tt_reward.py
+1 -1
View File
@@ -19,7 +19,7 @@ class TT_Reward:
# # seems to work for episodic case
min_r_b_dist = np.min(np.linalg.norm(np.array(self.c_ball_traj) - np.array(self.c_racket_traj), axis=1))
if not hited_ball:
return 0.2 * (1- np.tanh(min_r_b_dist**2))
return 0.2 * (1 - np.tanh(min_r_b_dist**2))
else:
if ball_landing_pos is None:
min_b_des_b_dist = np.min(np.linalg.norm(np.array(self.c_ball_traj)[:,:2] - self.c_goal[:2], axis=1))