255 lines
10 KiB
Python
255 lines
10 KiB
Python
import copy
|
|
import os
|
|
|
|
import numpy as np
|
|
from gym.envs.mujoco.hopper_v4 import HopperEnv
|
|
MAX_EPISODE_STEPS_HOPPERJUMP = 250
|
|
|
|
|
|
class HopperJumpEnv(HopperEnv):
|
|
"""
|
|
Initialization changes to normal Hopper:
|
|
- terminate_when_unhealthy: True -> False
|
|
- healthy_reward: 1.0 -> 2.0
|
|
- healthy_z_range: (0.7, float('inf')) -> (0.5, float('inf'))
|
|
- healthy_angle_range: (-0.2, 0.2) -> (-float('inf'), float('inf'))
|
|
- exclude_current_positions_from_observation: True -> False
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
xml_file='hopper_jump.xml',
|
|
forward_reward_weight=1.0,
|
|
ctrl_cost_weight=1e-3,
|
|
healthy_reward=2.0,
|
|
contact_weight=2.0,
|
|
height_weight=10.0,
|
|
dist_weight=3.0,
|
|
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')),
|
|
reset_noise_scale=5e-3,
|
|
exclude_current_positions_from_observation=False,
|
|
sparse=False,
|
|
):
|
|
|
|
self.sparse = sparse
|
|
self._height_weight = height_weight
|
|
self._dist_weight = dist_weight
|
|
self._contact_weight = contact_weight
|
|
|
|
self.max_height = 0
|
|
self.goal = np.zeros(3, )
|
|
|
|
self._steps = 0
|
|
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=xml_file,
|
|
forward_reward_weight=forward_reward_weight,
|
|
ctrl_cost_weight=ctrl_cost_weight,
|
|
healthy_reward=healthy_reward,
|
|
terminate_when_unhealthy=terminate_when_unhealthy,
|
|
healthy_state_range=healthy_state_range,
|
|
healthy_z_range=healthy_z_range,
|
|
healthy_angle_range=healthy_angle_range,
|
|
reset_noise_scale=reset_noise_scale,
|
|
exclude_current_positions_from_observation=exclude_current_positions_from_observation)
|
|
|
|
# increase initial height
|
|
self.init_qpos[1] = 1.5
|
|
|
|
@property
|
|
def exclude_current_positions_from_observation(self):
|
|
return self._exclude_current_positions_from_observation
|
|
|
|
def step(self, action):
|
|
self._steps += 1
|
|
|
|
self.do_simulation(action, self.frame_skip)
|
|
|
|
height_after = self.get_body_com("torso")[2]
|
|
#site_pos_after = self.data.get_site_xpos('foot_site')
|
|
site_pos_after = self.data.site('foot_site').xpos
|
|
self.max_height = max(height_after, self.max_height)
|
|
|
|
has_floor_contact = self._is_floor_foot_contact() if not self.contact_with_floor else False
|
|
|
|
if not self.init_floor_contact:
|
|
self.init_floor_contact = has_floor_contact
|
|
if self.init_floor_contact and not self.has_left_floor:
|
|
self.has_left_floor = not has_floor_contact
|
|
if not self.contact_with_floor and self.has_left_floor:
|
|
self.contact_with_floor = has_floor_contact
|
|
|
|
ctrl_cost = self.control_cost(action)
|
|
costs = ctrl_cost
|
|
done = False
|
|
|
|
goal_dist = np.linalg.norm(site_pos_after - self.goal)
|
|
if self.contact_dist is None and self.contact_with_floor:
|
|
self.contact_dist = goal_dist
|
|
|
|
rewards = 0
|
|
if not self.sparse or (self.sparse and self._steps >= MAX_EPISODE_STEPS_HOPPERJUMP):
|
|
healthy_reward = self.healthy_reward
|
|
distance_reward = -goal_dist * self._dist_weight
|
|
height_reward = (self.max_height if self.sparse else self.get_body_com("torso")[2]) * self._height_weight
|
|
contact_reward = -(self.contact_dist or 5) * self._contact_weight
|
|
rewards = self._forward_reward_weight * (distance_reward + height_reward + contact_reward + healthy_reward)
|
|
|
|
observation = self._get_obs()
|
|
reward = rewards - costs
|
|
info = dict(
|
|
height=height_after,
|
|
x_pos=site_pos_after,
|
|
max_height=self.max_height,
|
|
goal=self.goal[:1],
|
|
goal_dist=goal_dist,
|
|
height_rew=self.max_height,
|
|
healthy_reward=self.healthy_reward * 2,
|
|
healthy=self.is_healthy,
|
|
contact_dist=self.contact_dist or 0
|
|
)
|
|
return observation, reward, done, info
|
|
|
|
def _get_obs(self):
|
|
# goal_dist = self.data.get_site_xpos('foot_site') - self.goal
|
|
goal_dist = self.data.site('foot_site').xpos - self.goal
|
|
return np.concatenate((super(HopperJumpEnv, self)._get_obs(), goal_dist.copy(), self.goal[:1]))
|
|
|
|
def reset_model(self):
|
|
super(HopperJumpEnv, self).reset_model()
|
|
|
|
# self.goal = self.np_random.uniform(0.3, 1.35, 1)[0]
|
|
self.goal = np.concatenate([self.np_random.uniform(0.3, 1.35, 1), np.zeros(2, )])
|
|
# self.sim.model.body_pos[self.sim.model.body_name2id('goal_site_body')] = self.goal
|
|
self.data.body('goal_site_body').xpos[:] = np.copy(self.goal)
|
|
self.max_height = 0
|
|
self._steps = 0
|
|
|
|
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
|
|
|
|
qpos = (
|
|
self.np_random.uniform(low=noise_low, high=noise_high, size=self.model.nq) +
|
|
self.init_qpos
|
|
)
|
|
qvel = (
|
|
# self.np_random.uniform(low=noise_low, high=noise_high, size=self.model.nv) +
|
|
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 _is_floor_foot_contact(self):
|
|
# floor_geom_id = self.model.geom_name2id('floor')
|
|
# foot_geom_id = self.model.geom_name2id('foot_geom')
|
|
# TODO: do this properly over a sensor in the xml file, see dmc hopper
|
|
floor_geom_id = self._mujoco_bindings.mj_name2id(self.model,
|
|
self._mujoco_bindings.mjtObj.mjOBJ_GEOM,
|
|
'floor')
|
|
foot_geom_id = self._mujoco_bindings.mj_name2id(self.model,
|
|
self._mujoco_bindings.mjtObj.mjOBJ_GEOM,
|
|
'foot_geom')
|
|
for i in range(self.data.ncon):
|
|
contact = self.data.contact[i]
|
|
collision = contact.geom1 == floor_geom_id and contact.geom2 == foot_geom_id
|
|
collision_trans = contact.geom1 == foot_geom_id and contact.geom2 == floor_geom_id
|
|
if collision or collision_trans:
|
|
return True
|
|
return False
|
|
|
|
# TODO is that needed? if so test it
|
|
class HopperJumpStepEnv(HopperJumpEnv):
|
|
|
|
def __init__(self,
|
|
xml_file='hopper_jump.xml',
|
|
forward_reward_weight=1.0,
|
|
ctrl_cost_weight=1e-3,
|
|
healthy_reward=1.0,
|
|
height_weight=3,
|
|
dist_weight=3,
|
|
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')),
|
|
reset_noise_scale=5e-3,
|
|
exclude_current_positions_from_observation=False
|
|
):
|
|
|
|
self._height_weight = height_weight
|
|
self._dist_weight = dist_weight
|
|
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)
|
|
|
|
def step(self, action):
|
|
self._steps += 1
|
|
|
|
self.do_simulation(action, self.frame_skip)
|
|
|
|
height_after = self.get_body_com("torso")[2]
|
|
site_pos_after = self.data.site('foot_site').xpos.copy()
|
|
self.max_height = max(height_after, self.max_height)
|
|
|
|
ctrl_cost = self.control_cost(action)
|
|
healthy_reward = self.healthy_reward
|
|
height_reward = self._height_weight * height_after
|
|
goal_dist = np.linalg.norm(site_pos_after - np.array([self.goal, 0, 0]))
|
|
goal_dist_reward = -self._dist_weight * goal_dist
|
|
dist_reward = self._forward_reward_weight * (goal_dist_reward + height_reward)
|
|
|
|
rewards = dist_reward + healthy_reward
|
|
costs = ctrl_cost
|
|
done = False
|
|
|
|
# This is only for logging the distance to goal when first having the contact
|
|
has_floor_contact = self._is_floor_foot_contact() if not self.contact_with_floor else False
|
|
|
|
if not self.init_floor_contact:
|
|
self.init_floor_contact = has_floor_contact
|
|
if self.init_floor_contact and not self.has_left_floor:
|
|
self.has_left_floor = not has_floor_contact
|
|
if not self.contact_with_floor and self.has_left_floor:
|
|
self.contact_with_floor = has_floor_contact
|
|
|
|
if self.contact_dist is None and self.contact_with_floor:
|
|
self.contact_dist = goal_dist
|
|
|
|
##############################################################
|
|
|
|
observation = self._get_obs()
|
|
reward = rewards - costs
|
|
info = {
|
|
'height': height_after,
|
|
'x_pos': site_pos_after,
|
|
'max_height': copy.copy(self.max_height),
|
|
'goal': copy.copy(self.goal),
|
|
'goal_dist': goal_dist,
|
|
'height_rew': height_reward,
|
|
'healthy_reward': healthy_reward,
|
|
'healthy': copy.copy(self.is_healthy),
|
|
'contact_dist': copy.copy(self.contact_dist) or 0
|
|
}
|
|
return observation, reward, done, info
|