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bp 2fs seems to work

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This commit is contained in:
Onur
2022-05-31 19:41:08 +02:00
parent 2ea7f6a2ed
commit 59b15e82ea
5 changed files with 98 additions and 15 deletions
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alr_envs/alr/mujoco/hopper_jump/hopper_jump.py
+41 -11
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@@ -120,7 +120,6 @@ class ALRHopperJumpEnv(HopperEnv):
class ALRHopperXYJumpEnv(ALRHopperJumpEnv):
# The goal here is the desired x-Position of the Torso
def step(self, action):
@@ -154,20 +153,15 @@ class ALRHopperXYJumpEnv(ALRHopperJumpEnv):
ctrl_cost = self.control_cost(action)
costs = ctrl_cost
done = False
goal_dist = 0
goal_dist = np.atleast_1d(np.linalg.norm(site_pos_after - np.array([self.goal, 0, 0], dtype=object)))[0]
rewards = 0
if self.current_step >= self.max_episode_steps:
# healthy_reward = 0 if self.context else self.healthy_reward * self.current_step
healthy_reward = self.healthy_reward * 2 #* self.current_step
goal_dist = np.linalg.norm(site_pos_after - np.array([self.goal, 0, 0], dtype=object))[0]
contact_dist = self.contact_dist if self.contact_dist is not None else 5
dist_reward = self._forward_reward_weight * (-3*goal_dist + 10*self.max_height - 2*contact_dist)
rewards = dist_reward + healthy_reward
# else:
## penalty for wrong start direction of first two joints; not needed, could be removed
# rewards = ((action[:2] > 0) * self.penalty).sum() if self.current_step < 10 else 0
observation = self._get_obs()
reward = rewards - costs
info = {
@@ -175,11 +169,10 @@ class ALRHopperXYJumpEnv(ALRHopperJumpEnv):
'x_pos': site_pos_after,
'max_height': self.max_height,
'goal': self.goal,
'dist_rew': goal_dist,
'goal_dist': goal_dist,
'height_rew': self.max_height,
'healthy_reward': self.healthy_reward * 2
}
return observation, reward, done, info
def reset_model(self):
@@ -213,12 +206,48 @@ class ALRHopperXYJumpEnv(ALRHopperJumpEnv):
def reset(self):
super().reset()
# self.goal = np.random.uniform(-1.5, 1.5, 1)
self.goal = np.random.uniform(0, 1.5, 1)
# self.goal = np.random.uniform(0, 1.5, 1)
self.goal = self.np_random.uniform(0, 1.5, 1)
# self.goal = np.array([1.5])
self.sim.model.body_pos[self.sim.model.body_name2id('goal_site_body')] = np.array([self.goal, 0, 0], dtype=object)
return self.reset_model()
class ALRHopperXYJumpEnvStepBased(ALRHopperXYJumpEnv):
def step(self, action):
self._floor_geom_id = self.model.geom_name2id('floor')
self._foot_geom_id = self.model.geom_name2id('foot_geom')
self.current_step += 1
self.do_simulation(action, self.frame_skip)
height_after = self.get_body_com("torso")[2]
site_pos_after = self.sim.data.site_xpos[self.model.site_name2id('foot_site')].copy()
self.max_height = max(height_after, self.max_height)
ctrl_cost = self.control_cost(action)
healthy_reward = self.healthy_reward * 2
height_reward = 10*height_after
goal_dist = np.atleast_1d(np.linalg.norm(site_pos_after - np.array([self.goal, 0, 0], dtype=object)))[0]
goal_dist_reward = -3*goal_dist
dist_reward = self._forward_reward_weight * (goal_dist_reward + height_reward)
reward = -ctrl_cost + healthy_reward + dist_reward
done = False
observation = self._get_obs()
info = {
'height': height_after,
'x_pos': site_pos_after,
'max_height': self.max_height,
'goal': self.goal,
'goal_dist': goal_dist,
'dist_rew': dist_reward,
'height_rew': height_reward,
'healthy_reward': healthy_reward,
'ctrl_reward': -ctrl_cost
}
return observation, reward, done, info
class ALRHopperJumpRndmPosEnv(ALRHopperJumpEnv):
def __init__(self, max_episode_steps=250):
super(ALRHopperJumpRndmPosEnv, self).__init__(exclude_current_positions_from_observation=False,
@@ -293,7 +322,8 @@ class ALRHopperJumpRndmPosEnv(ALRHopperJumpEnv):
if __name__ == '__main__':
render_mode = "human" # "human" or "partial" or "final"
# env = ALRHopperJumpEnv()
env = ALRHopperXYJumpEnv()
# env = ALRHopperXYJumpEnv()
env = ALRHopperXYJumpEnvStepBased()
# env = ALRHopperJumpRndmPosEnv()
obs = env.reset()