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hopper_v3.py
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import numpy as np
from gym.envs.mujoco import mujoco_env
from gym import utils
DEFAULT_CAMERA_CONFIG = {
'trackbodyid': 2,
'distance': 3.0,
'lookat': np.array((0.0, 0.0, 1.15)),
'elevation': -20.0,
}
class HopperEnv(mujoco_env.MujocoEnv, utils.EzPickle):
def __init__(self,
xml_file='hopper.xml',
forward_reward_weight=1.0,
ctrl_cost_weight=1e-3,
healthy_reward=1.0,
terminate_when_unhealthy=True,
healthy_state_range=(-100.0, 100.0),
healthy_z_range=(0.7, float('inf')),
healthy_angle_range=(-0.2, 0.2),
reset_noise_scale=5e-3,
exclude_current_positions_from_observation=True):
utils.EzPickle.__init__(**locals())
self._forward_reward_weight = forward_reward_weight
self._ctrl_cost_weight = ctrl_cost_weight
self._healthy_reward = healthy_reward
self._terminate_when_unhealthy = terminate_when_unhealthy
self._healthy_state_range = healthy_state_range
self._healthy_z_range = healthy_z_range
self._healthy_angle_range = healthy_angle_range
self._reset_noise_scale = reset_noise_scale
self._exclude_current_positions_from_observation = (
exclude_current_positions_from_observation)
mujoco_env.MujocoEnv.__init__(self, xml_file, 4)
@property
def healthy_reward(self):
return float(
self.is_healthy
or self._terminate_when_unhealthy
) * self._healthy_reward
def control_cost(self, action):
control_cost = self._ctrl_cost_weight * np.sum(np.square(action))
return control_cost
@property
def is_healthy(self):
z, angle = self.sim.data.qpos[1:3]
state = self.state_vector()[2:]
min_state, max_state = self._healthy_state_range
min_z, max_z = self._healthy_z_range
min_angle, max_angle = self._healthy_angle_range
healthy_state = np.all(
np.logical_and(min_state < state, state < max_state))
healthy_z = min_z < z < max_z
healthy_angle = min_angle < angle < max_angle
is_healthy = all((healthy_state, healthy_z, healthy_angle))
return is_healthy
@property
def done(self):
done = (not self.is_healthy
if self._terminate_when_unhealthy
else False)
return done
def _get_obs(self):
position = self.sim.data.qpos.flat.copy()
velocity = np.clip(
self.sim.data.qvel.flat.copy(), -10, 10)
if self._exclude_current_positions_from_observation:
position = position[1:]
observation = np.concatenate((position, velocity)).ravel()
return observation
def step(self, action):
x_position_before = self.sim.data.qpos[0]
self.do_simulation(action, self.frame_skip)
x_position_after = self.sim.data.qpos[0]
x_velocity = ((x_position_after - x_position_before)
/ self.dt)
ctrl_cost = self.control_cost(action)
forward_reward = self._forward_reward_weight * x_velocity
healthy_reward = self.healthy_reward
rewards = forward_reward + healthy_reward
costs = ctrl_cost
observation = self._get_obs()
reward = rewards - costs
done = self.done
info = {
'x_position': x_position_after,
'x_velocity': x_velocity,
}
return observation, reward, done, info
def reset_model(self):
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)
self.set_state(qpos, qvel)
observation = self._get_obs()
return observation
def viewer_setup(self):
for key, value in DEFAULT_CAMERA_CONFIG.items():
if isinstance(value, np.ndarray):
getattr(self.viewer.cam, key)[:] = value
else:
setattr(self.viewer.cam, key, value)