import copy
import os
import numpy as np
from gym.envs.mujoco.hopper_v3 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, 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)
# 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')
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
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.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')
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
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.get_site_xpos('foot_site')
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