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This commit is contained in:
Onur
2022-05-29 11:58:01 +02:00
parent 647f086a8d
commit f1a96c055b
12 changed files with 333 additions and 168 deletions
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alr_envs/alr/mujoco/hopper_jump/assets/hopper.xml → alr_envs/alr/mujoco/hopper_jump/assets/hopper_jump.xml
+4
View File
@@ -25,12 +25,16 @@
<joint axis="0 -1 0" name="leg_joint" pos="0 0 0.6" range="-150 0" type="hinge"/>
<geom friction="0.9" fromto="0 0 0.6 0 0 0.1" name="leg_geom" size="0.04" type="capsule"/>
<body name="foot" pos="0.13/2 0 0.1">
<site name="foot_site" pos="0 0 0.04" size="0.02 0.02 0.02" rgba="1 0 0 1" type="sphere"/>
<joint axis="0 -1 0" name="foot_joint" pos="0 0 0.1" range="-45 45" type="hinge"/>
<geom friction="2.0" fromto="-0.13 0 0.1 0.26 0 0.1" name="foot_geom" size="0.06" type="capsule"/>
</body>
</body>
</body>
</body>
<body name="goal_site_body" pos = "0 0 0">
<site name="goal_site" pos="0 0 0.0" size="0.02 0.02 0.02" rgba="0 1 0 1" type="sphere"/>
</body>
</worldbody>
<actuator>
<motor ctrllimited="true" ctrlrange="-1.0 1.0" gear="200.0" joint="thigh_joint"/>
alr_envs/alr/mujoco/hopper_jump/hopper_jump.py
+149 -29
View File
@@ -1,5 +1,6 @@
from gym.envs.mujoco.hopper_v3 import HopperEnv
import numpy as np
import os
MAX_EPISODE_STEPS_HOPPERJUMP = 250
@@ -14,13 +15,13 @@ class ALRHopperJumpEnv(HopperEnv):
"""
def __init__(self,
xml_file='hopper.xml',
xml_file='hopper_jump.xml',
forward_reward_weight=1.0,
ctrl_cost_weight=1e-3,
healthy_reward=0.0,
penalty=0.0,
context=True,
terminate_when_unhealthy=True,
terminate_when_unhealthy=False,
healthy_state_range=(-100.0, 100.0),
healthy_z_range=(0.5, float('inf')),
healthy_angle_range=(-float('inf'), float('inf')),
@@ -34,6 +35,13 @@ class ALRHopperJumpEnv(HopperEnv):
self.goal = 0
self.context = context
self.exclude_current_positions_from_observation = exclude_current_positions_from_observation
self._floor_geom_id = None
self._foot_geom_id = None
self.contact_with_floor = False
self.init_floor_contact = False
self.has_left_floor = False
self.contact_dist = None
xml_file = os.path.join(os.path.dirname(__file__), "assets", xml_file)
super().__init__(xml_file, forward_reward_weight, ctrl_cost_weight, healthy_reward, terminate_when_unhealthy,
healthy_state_range, healthy_z_range, healthy_angle_range, reset_noise_scale,
exclude_current_positions_from_observation)
@@ -43,28 +51,36 @@ class ALRHopperJumpEnv(HopperEnv):
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)
costs = ctrl_cost
done = False
rewards = 0
if self.current_step >= self.max_episode_steps:
hight_goal_distance = -10*np.linalg.norm(self.max_height - self.goal) if self.context else self.max_height
healthy_reward = 0 if self.context else self.healthy_reward * self.current_step
healthy_reward = 0 if self.context else self.healthy_reward * 2 # self.current_step
height_reward = self._forward_reward_weight * hight_goal_distance # maybe move reward calculation into if structure and define two different _forward_reward_weight variables for context and episodic seperatley
rewards = height_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
# 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 = {
# 'height' : height_after,
# 'max_height': self.max_height,
# 'goal' : self.goal
# }
info = {
'height' : height_after,
'height': height_after,
'x_pos': site_pos_after,
'max_height': self.max_height,
'goal' : self.goal
'height_rew': self.max_height,
'healthy_reward': self.healthy_reward * 2
}
return observation, reward, done, info
@@ -73,7 +89,7 @@ class ALRHopperJumpEnv(HopperEnv):
return np.append(super()._get_obs(), self.goal)
def reset(self):
self.goal = np.random.uniform(1.4, 2.3, 1) # 1.3 2.3
self.goal = np.random.uniform(1.4, 2.16, 1) # 1.3 2.3
self.max_height = 0
self.current_step = 0
return super().reset()
@@ -87,14 +103,124 @@ class ALRHopperJumpEnv(HopperEnv):
self.set_state(qpos, qvel)
observation = self._get_obs()
self.has_left_floor = False
self.contact_with_floor = False
self.init_floor_contact = False
self.contact_dist = None
return observation
def _contact_checker(self, id_1, id_2):
for coni in range(0, self.sim.data.ncon):
con = self.sim.data.contact[coni]
collision = con.geom1 == id_1 and con.geom2 == id_2
collision_trans = con.geom1 == id_2 and con.geom2 == id_1
if collision or collision_trans:
return True
return False
class ALRHopperXYJumpEnv(ALRHopperJumpEnv):
# The goal here is the desired x-Position of the Torso
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)
# floor_contact = self._contact_checker(self._floor_geom_id, self._foot_geom_id) if not self.contact_with_floor else False
# self.init_floor_contact = floor_contact if not self.init_floor_contact else self.init_floor_contact
# self.has_left_floor = not floor_contact if self.init_floor_contact and not self.has_left_floor else self.has_left_floor
# self.contact_with_floor = floor_contact if not self.contact_with_floor and self.has_left_floor else self.contact_with_floor
floor_contact = self._contact_checker(self._floor_geom_id,
self._foot_geom_id) if not self.contact_with_floor else False
if not self.init_floor_contact:
self.init_floor_contact = floor_contact
if self.init_floor_contact and not self.has_left_floor:
self.has_left_floor = not floor_contact
if not self.contact_with_floor and self.has_left_floor:
self.contact_with_floor = floor_contact
if self.contact_dist is None and self.contact_with_floor:
self.contact_dist = np.linalg.norm(self.sim.data.site_xpos[self.model.site_name2id('foot_site')]
- np.array([self.goal, 0, 0], dtype=object))[0]
ctrl_cost = self.control_cost(action)
costs = ctrl_cost
done = False
goal_dist = 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 = {
'height': height_after,
'x_pos': site_pos_after,
'max_height': self.max_height,
'goal': self.goal,
'dist_rew': goal_dist,
'height_rew': self.max_height,
'healthy_reward': self.healthy_reward * 2
}
return observation, reward, done, info
def reset_model(self):
self.init_qpos[1] = 1.5
self._floor_geom_id = self.model.geom_name2id('floor')
self._foot_geom_id = self.model.geom_name2id('foot_geom')
noise_low = -np.zeros(self.model.nq)
noise_low[3] = -0.5
noise_low[4] = -0.2
noise_low[5] = 0
noise_high = np.zeros(self.model.nq)
noise_high[3] = 0
noise_high[4] = 0
noise_high[5] = 0.785
rnd_vec = self.np_random.uniform(low=noise_low, high=noise_high, size=self.model.nq)
qpos = self.init_qpos + rnd_vec
qvel = self.init_qvel
self.set_state(qpos, qvel)
observation = self._get_obs()
self.has_left_floor = False
self.contact_with_floor = False
self.init_floor_contact = False
self.contact_dist = None
return observation
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.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 ALRHopperJumpRndmPosEnv(ALRHopperJumpEnv):
def __init__(self, max_episode_steps=250):
self.contact_with_floor = False
self._floor_geom_id = None
self._foot_geom_id = None
super(ALRHopperJumpRndmPosEnv, self).__init__(exclude_current_positions_from_observation=False,
reset_noise_scale=5e-1,
max_episode_steps=max_episode_steps)
@@ -104,17 +230,17 @@ class ALRHopperJumpRndmPosEnv(ALRHopperJumpEnv):
self._foot_geom_id = self.model.geom_name2id('foot_geom')
noise_low = -np.ones(self.model.nq)*self._reset_noise_scale
noise_low[1] = 0
noise_low[2] = -0.3
noise_low[3] = -0.1
noise_low[4] = -1.1
noise_low[5] = -0.785
noise_low[2] = 0
noise_low[3] = -0.2
noise_low[4] = -0.2
noise_low[5] = -0.1
noise_high = np.ones(self.model.nq)*self._reset_noise_scale
noise_high[1] = 0
noise_high[2] = 0.3
noise_high[2] = 0
noise_high[3] = 0
noise_high[4] = 0
noise_high[5] = 0.785
noise_high[5] = 0.1
rnd_vec = self.np_random.uniform(low=noise_low, high=noise_high, size=self.model.nq)
# rnd_vec[2] *= 0.05 # the angle around the y axis shouldn't be too high as the agent then falls down quickly and
@@ -162,24 +288,18 @@ class ALRHopperJumpRndmPosEnv(ALRHopperJumpEnv):
return observation, reward, done, info
def _contact_checker(self, id_1, id_2):
for coni in range(0, self.sim.data.ncon):
con = self.sim.data.contact[coni]
collision = con.geom1 == id_1 and con.geom2 == id_2
collision_trans = con.geom1 == id_2 and con.geom2 == id_1
if collision or collision_trans:
return True
return False
if __name__ == '__main__':
render_mode = "human" # "human" or "partial" or "final"
# env = ALRHopperJumpEnv()
env = ALRHopperJumpRndmPosEnv()
env = ALRHopperXYJumpEnv()
# env = ALRHopperJumpRndmPosEnv()
obs = env.reset()
for k in range(10):
obs = env.reset()
print('observation :', obs[:6])
print('observation :', obs[:])
for i in range(200):
# objective.load_result("/tmp/cma")
# test with random actions
alr_envs/alr/mujoco/hopper_jump/new_mp_wrapper.py
+11 -1
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@@ -12,9 +12,19 @@ class NewMPWrapper(EpisodicWrapper):
def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
return self.env.sim.data.qvel[3:6].copy()
# # random goal
# def set_active_obs(self):
# return np.hstack([
# [False] * (5 + int(not self.env.exclude_current_positions_from_observation)), # position
# [False] * 6, # velocity
# [True]
# ])
# Random x goal + random init pos
def set_active_obs(self):
return np.hstack([
[False] * (5 + int(not self.env.exclude_current_positions_from_observation)), # position
[False] * (2 + int(not self.env.exclude_current_positions_from_observation)), # position
[True] * 3, # set to true if randomize initial pos
[False] * 6, # velocity
[True]
])