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

hopper throw seeding fixed

Browse Source
This commit is contained in:
Fabian 2022-07-12 14:18:01 +02:00
parent 5d4fc4d52f
commit 993df10fad
2 changed files with 23 additions and 21 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

21
alr_envs/alr/mujoco/hopper_throw/hopper_throw.py
View File

@ -27,7 +27,7 @@ class ALRHopperThrowEnv(HopperEnv):
healthy_z_range=(0.7, float('inf')),
healthy_angle_range=(-float('inf'), float('inf')),
reset_noise_scale=5e-3,
context = True,
context=True,
exclude_current_positions_from_observation=True,
max_episode_steps=250):
xml_file = os.path.join(os.path.dirname(__file__), "assets", xml_file)
@ -40,10 +40,10 @@ class ALRHopperThrowEnv(HopperEnv):
exclude_current_positions_from_observation)
def step(self, action):
self.current_step += 1
self.do_simulation(action, self.frame_skip)
ball_pos_after = self.get_body_com("ball")[0] #abs(self.get_body_com("ball")[0]) # use x and y to get point and use euclid distance as reward?
ball_pos_after = self.get_body_com("ball")[
0] # abs(self.get_body_com("ball")[0]) # use x and y to get point and use euclid distance as reward?
ball_pos_after_y = self.get_body_com("ball")[2]
# done = self.done TODO We should use this, not sure why there is no other termination; ball_landed should be enough, because we only look at the throw itself? - Paul and Marc
@ -57,7 +57,7 @@ class ALRHopperThrowEnv(HopperEnv):
if self.current_step >= self.max_episode_steps or done:
distance_reward = -np.linalg.norm(ball_pos_after - self.goal) if self.context else \
self._forward_reward_weight * ball_pos_after
self._forward_reward_weight * ball_pos_after
healthy_reward = 0 if self.context else self.healthy_reward * self.current_step
rewards = distance_reward + healthy_reward
@ -67,8 +67,8 @@ class ALRHopperThrowEnv(HopperEnv):
info = {
'ball_pos': ball_pos_after,
'ball_pos_y': ball_pos_after_y,
'_steps' : self.current_step,
'goal' : self.goal,
'_steps': self.current_step,
'goal': self.goal,
}
return observation, reward, done, info
@ -78,7 +78,7 @@ class ALRHopperThrowEnv(HopperEnv):
def reset(self):
self.current_step = 0
self.goal = self.goal = np.random.uniform(2.0, 6.0, 1) # 0.5 8.0
self.goal = self.goal = self.np_random.uniform(2.0, 6.0, 1) # 0.5 8.0
return super().reset()
# overwrite reset_model to make it deterministic
@ -86,14 +86,15 @@ class ALRHopperThrowEnv(HopperEnv):
noise_low = -self._reset_noise_scale
noise_high = self._reset_noise_scale
qpos = self.init_qpos # + self.np_random.uniform(low=noise_low, high=noise_high, size=self.model.nq)
qvel = self.init_qvel # + self.np_random.uniform(low=noise_low, high=noise_high, size=self.model.nv)
qpos = self.init_qpos # + self.np_random.uniform(low=noise_low, high=noise_high, size=self.model.nq)
qvel = self.init_qvel # + self.np_random.uniform(low=noise_low, high=noise_high, size=self.model.nv)
self.set_state(qpos, qvel)
observation = self._get_obs()
return observation
if __name__ == '__main__':
render_mode = "human" # "human" or "partial" or "final"
env = ALRHopperThrowEnv()
@ -110,4 +111,4 @@ if __name__ == '__main__':
print('After ', i, ' steps, done: ', d)
env.reset()
env.close()
env.close()

23
alr_envs/alr/mujoco/hopper_throw/hopper_throw_in_basket.py
View File

@ -3,7 +3,6 @@ from gym.envs.mujoco.hopper_v3 import HopperEnv
import numpy as np
MAX_EPISODE_STEPS_HOPPERTHROWINBASKET = 250
@ -33,7 +32,7 @@ class ALRHopperThrowInBasketEnv(HopperEnv):
context=True,
penalty=0.0,
exclude_current_positions_from_observation=True,
max_episode_steps = 250):
max_episode_steps=250):
self.hit_basket_reward = hit_basket_reward
self.current_step = 0
self.max_episode_steps = max_episode_steps
@ -57,7 +56,8 @@ class ALRHopperThrowInBasketEnv(HopperEnv):
basket_center = (basket_pos[0] + 0.5, basket_pos[1], basket_pos[2])
is_in_basket_x = ball_pos[0] >= basket_pos[0] and ball_pos[0] <= basket_pos[0] + self.basket_size
is_in_basket_y = ball_pos[1] >= basket_pos[1] - (self.basket_size/2) and ball_pos[1] <= basket_pos[1] + (self.basket_size/2)
is_in_basket_y = ball_pos[1] >= basket_pos[1] - (self.basket_size / 2) and ball_pos[1] <= basket_pos[1] + (
self.basket_size / 2)
is_in_basket_z = ball_pos[2] < 0.1
is_in_basket = is_in_basket_x and is_in_basket_y and is_in_basket_z
if is_in_basket: self.ball_in_basket = True
@ -77,15 +77,16 @@ class ALRHopperThrowInBasketEnv(HopperEnv):
if not self.context:
rewards += self.hit_basket_reward
else:
dist = np.linalg.norm(ball_pos-basket_center)
dist = np.linalg.norm(ball_pos - basket_center)
if self.context:
rewards = -10 * dist
else:
rewards -= (dist*dist)
rewards -= (dist * dist)
else:
# penalty not needed
rewards += ((action[:2] > 0) * self.penalty).sum() if self.current_step < 10 else 0 #too much of a penalty?
rewards += ((action[
:2] > 0) * self.penalty).sum() if self.current_step < 10 else 0 # too much of a penalty?
observation = self._get_obs()
reward = rewards - costs
info = {
@ -106,7 +107,7 @@ class ALRHopperThrowInBasketEnv(HopperEnv):
self.ball_in_basket = False
if self.context:
basket_id = self.sim.model.body_name2id("basket_ground")
self.basket_x = np.random.uniform(3, 7, 1)
self.basket_x = self.np_random.uniform(3, 7, 1)
self.sim.model.body_pos[basket_id] = [self.basket_x, 0, 0]
return super().reset()
@ -115,8 +116,8 @@ class ALRHopperThrowInBasketEnv(HopperEnv):
noise_low = -self._reset_noise_scale
noise_high = self._reset_noise_scale
qpos = self.init_qpos # + self.np_random.uniform(low=noise_low, high=noise_high, size=self.model.nq)
qvel = self.init_qvel # + self.np_random.uniform(low=noise_low, high=noise_high, size=self.model.nv)
qpos = self.init_qpos # + self.np_random.uniform(low=noise_low, high=noise_high, size=self.model.nq)
qvel = self.init_qvel # + self.np_random.uniform(low=noise_low, high=noise_high, size=self.model.nv)
self.set_state(qpos, qvel)
@ -140,4 +141,4 @@ if __name__ == '__main__':
print('After ', i, ' steps, done: ', d)
env.reset()
env.close()
env.close()