class HalfCheetahBulletEnv(WalkerBaseBulletEnv):
def __init__(self, render=False):
self.robot = HalfCheetah()
WalkerBaseBulletEnv.__init__(self, self.robot, render)
print(
"PyBullet Halfcheetah: reward = progress + (njoint_at_limit * -0.1), terminate also when z < 0.3"
)
def _isDone(self):
return False
def step(self, a):
if not self.scene.multiplayer: # if multiplayer, action first applied to all robots, then global step() called, then _step() for all robots with the same actions
self.robot.apply_action(a)
self.scene.global_step()
state = self.robot.calc_state() # also calculates self.joints_at_limit
self._alive = float(
self.robot.alive_bonus(state[0] + self.robot.initial_z,
self.robot.body_rpy[1])
) # state[0] is body height above ground, body_rpy[1] is pitch
done = self._isDone()
if not np.isfinite(state).all():
print("~INF~", state)
done = True
potential_old = self.potential
self.potential = self.robot.calc_potential()
progress = float(self.potential - potential_old)
feet_collision_cost = 0.0
for i, f in enumerate(
self.robot.feet
): # TODO: Maybe calculating feet contacts could be done within the robot code
contact_ids = set((x[2], x[4]) for x in f.contact_list())
#print("CONTACT OF '%d' WITH %d" % (contact_ids, ",".join(contact_names)) )
if (self.ground_ids & contact_ids):
#see Issue 63: https://github.com/openai/roboschool/issues/63
#feet_collision_cost += self.foot_collision_cost
self.robot.feet_contact[i] = 1.0
else:
self.robot.feet_contact[i] = 0.0
joints_at_limit_cost = float(self.joints_at_limit_cost *
self.robot.joints_at_limit)
self.HUD(state, a, done)
if (self._alive < 0):
return state, progress + joints_at_limit_cost, True, {
"progress": progress
}
else:
return state, progress + joints_at_limit_cost, False, {
"progress": progress
}
class CrippledHalfCheetahBulletEnv(WalkerBaseBulletEnv):
def __init__(self, render=False):
self.robot = HalfCheetah()
WalkerBaseBulletEnv.__init__(self, self.robot, render)
cripple_prob = [0.5, 1 / 12, 1 / 12, 1 / 12, 1 / 12, 1 / 12,
1 / 12] # None .40, 0-4 0.15 each
self.crippled_joint = np.random.choice([None, 0, 1, 2, 3, 4, 5],
p=cripple_prob)
self.gates = [
np.where(np.random.uniform(0, 1, size=(48, )) >= 0.2, 1.0, 0.0)
for _ in range(7)
]
def reset(self):
if (self.stateId >= 0):
self._p.restoreState(self.stateId)
r = MJCFBaseBulletEnv.reset(self)
self._p.configureDebugVisualizer(pybullet.COV_ENABLE_RENDERING, 0)
self.parts, self.jdict, self.ordered_joints, self.robot_body = self.robot.addToScene(
self._p, self.stadium_scene.ground_plane_mjcf)
self.ground_ids = set([(self.parts[f].bodies[self.parts[f].bodyIndex],
self.parts[f].bodyPartIndex)
for f in self.foot_ground_object_names])
self._p.configureDebugVisualizer(pybullet.COV_ENABLE_RENDERING, 1)
if (self.stateId < 0):
self.stateId = self._p.saveState()
cripple_prob = [0.5, 1 / 12, 1 / 12, 1 / 12, 1 / 12, 1 / 12,
1 / 12] # None .40, 0-4 0.15 each
self.crippled_joint = np.random.choice([None, 0, 1, 2, 3, 4, 5],
p=cripple_prob)
self.gate = self.gates[self.crippled_joint +
1 if self.crippled_joint is not None else 0]
return r
def _isDone(self):
return self._alive < 0
electricity_cost = -2.0
stall_torque_cost = -0.1
foot_collision_cost = -1.0
joints_at_limit_cost = -0.1
foot_ground_object_names = set(["floor"])
def step(self, a):
if self.crippled_joint is not None:
a[self.crippled_joint] = 0
self.robot.apply_action(a)
self.scene.global_step()
state = self.robot.calc_state()
self._alive = float(
self.robot.alive_bonus(state[0] + self.robot.initial_z,
self.robot.body_rpy[1]))
done = self._isDone()
if not np.isfinite(state).all():
print("~INF~", state)
done = True
potential_old = self.potential
self.potential = self.robot.calc_potential()
progress = float(self.potential - potential_old)
feet_collision_cost = 0.0
for i, f in enumerate(self.robot.feet):
contact_ids = set((x[2], x[4]) for x in f.contact_list())
if (self.ground_ids & contact_ids):
self.robot.feet_contact[i] = 1.0
else:
self.robot.feet_contact[i] = 0.0
electricity_cost = self.electricity_cost * \
float(np.abs(a * self.robot.joint_speeds).mean())
electricity_cost += self.stall_torque_cost * \
float(np.square(a).mean())
joints_at_limit_cost_T = float(self.joints_at_limit_cost *
self.robot.joints_at_limit)
joints_at_limit_cost = float(-0.1 * self.robot.joints_at_limit)
self.rewards = [joints_at_limit_cost, progress]
self.HUD(state, a, done)
self.reward += sum(self.rewards)
return state, sum(self.rewards), bool(done), {}