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car.py
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car.py
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import numpy as np
import utils
import theano as th
import theano.tensor as tt
import theano.tensor.slinalg as ts
from trajectory import Trajectory
import feature
class Car(object):
def __init__(self, dyn, x0, color='yellow', T=5, iamrobot = False):
self.data0 = {'x0': x0}
self.bounds = [(-1., 1.), (-1., 1.)]
self.T = T
self.dyn = dyn
self.traj = Trajectory(T, dyn)
self.traj.x0.set_value(x0)
self.linear = Trajectory(T, dyn)
self.linear.x0.set_value(x0)
self.color = color
self.default_u = np.zeros(self.dyn.nu)
self.iamrobot = iamrobot
def reset(self):
self.traj.x0.set_value(self.data0['x0'])
self.linear.x0.set_value(self.data0['x0'])
for t in range(self.T):
self.traj.u[t].set_value(np.zeros(self.dyn.nu))
self.linear.u[t].set_value(self.default_u)
def move(self):
self.traj.tick()
self.linear.x0.set_value(self.traj.x0.get_value())
@property
def x(self):
return self.traj.x0.get_value()
@property
def u(self):
return self.traj.u[0].get_value()
@u.setter
def u(self, value):
self.traj.u[0].set_value(value)
def control(self, steer, gas):
pass
class UserControlledCar(Car):
def __init__(self, *args, **vargs):
Car.__init__(self, *args, **vargs)
self.bounds = [(-1., 1.), (-1., 1.)]
self.follow = None
self.fixed_control = None
self._fixed_control = None
def fix_control(self, ctrl):
self.fixed_control = ctrl
self._fixed_control = ctrl
def control(self, steer, gas):
if self.fixed_control is not None:
self.u = self.fixed_control[0]
print self.fixed_control[0]
if len(self.fixed_control)>1:
self.fixed_control = self.fixed_control[1:]
elif self.follow is None:
self.u = [steer, gas]
else:
u = self.follow.u[0].get_value()
if u[1]>=1.:
u[1] = 1.
if u[1]<=-1.:
u[1] = -1.
self.u = u
def reset(self):
Car.reset(self)
self.fixed_control = self._fixed_control
class SimpleOptimizerCar(Car):
def __init__(self, *args, **vargs):
Car.__init__(self, *args, **vargs)
self.bounds = [(-1., 1.), (-1., 1.)]
self.cache = []
self.index = 0
self.sync = lambda cache: None
def reset(self):
Car.reset(self)
self.index = 0
@property
def reward(self):
return self._reward
@reward.setter
def reward(self, reward):
self._reward = reward+100.*feature.bounded_control(self.bounds)
self.optimizer = None
def control(self, steer, gas):
print len(self.cache)
if self.index<len(self.cache):
self.u = self.cache[self.index]
else:
if self.optimizer is None:
r = self.traj.reward(self.reward)
self.optimizer = utils.Maximizer(r, self.traj.u)
self.optimizer.maximize()
self.cache.append(self.u)
self.sync(self.cache)
self.index += 1
class NestedOptimizerCar(Car):
def __init__(self, *args, **vargs):
Car.__init__(self, *args, **vargs)
self.bounds = [(-3., 3.), (-2., 2.)]
@property
def human(self):
return self._human
@human.setter
def human(self, value):
self._human = value
self.traj_h = Trajectory(self.T, self.human.dyn)
def move(self):
Car.move(self)
self.traj_h.tick()
@property
def rewards(self):
return self._rewards
@rewards.setter
def rewards(self, vals):
self._rewards = vals
self.optimizer = None
def control(self, steer, gas):
if self.optimizer is None:
reward_h, reward_r = self.rewards
reward_h = self.traj_h.reward(reward_h)
reward_r = self.traj.reward(reward_r)
self.optimizer = utils.NestedMaximizer(reward_h, self.traj_h.u, reward_r, self.traj.u)
self.traj_h.x0.set_value(self.human.x)
self.optimizer.maximize(bounds = self.bounds)
class SwitchOptimizerCar(Car):
def __init__(self, *args, **vargs):
Car.__init__(self, *args, **vargs)
self.bounds = [(-3., 3.), (-2., 2.)]
self.cache = []
self.index = 0
# set these to None to begin with (so we can compare against something that exists)
self._baseline_reward = None
self._simple_reward = None
self._nested_rewards = None
self._human = None
# start of not with nested optimizer
self._nested = False
# Car that the vehicle is platooning behind
self.platoon_behind = None
assert(self.iamrobot == True)
@property
def human(self):
return self._human
@human.setter
def human(self, value):
# Reinitialize the optimizer only if we are switching the human
if not (value == self._human):
self._human = value
#self.traj_h = Trajectory(self.T, self.human.dyn)
self.traj_h = Trajectory(self.T, self._human.dyn)
def move(self):
#if self.traj_h is not None:
# self.traj_h.tick()
if self._nested:
Car.move(self)
self.traj_h.tick()
else:
self.traj.tick()
self.linear.x0.set_value(self.traj.x0.get_value())
# set this baseline reward at car instantiation. The midlevel optimizer
# can use this to reset the car's reward after giving it specialized rewards
@property
def baseline_reward(self):
return self._baseline_reward
@baseline_reward.setter
def baseline_reward(self, reward):
self._baseline_reward = reward
self._simple_reward = reward
self.simple_optimizer = None
@property
def simple_reward(self):
return self._simple_reward
@simple_reward.setter
def simple_reward(self, reward):
# TODO: do we add bounded control here?
# If we already have the proper reward, no need to re-initialize the optimizer
if not (reward==self._simple_reward):
self._simple_reward = reward
self.simple_optimizer = None
@property
def nested_rewards(self):
return self._nested_rewards
@nested_rewards.setter
def nested_rewards(self, vals):
# reinitialize only if we are switching rewards
if not (vals == self._nested_rewards):
self._nested_rewards = vals
self.nested_optimizer = None
@property
def nested(self):
return self._nested
@nested.setter
def nested(self, status):
# make sure we don't have old cached controls
if not (status == self._nested):
self.index = 0
self.cache = []
# set internal status of whether to run simple or nested optimization
self._nested = status
def control(self, steer, gas):
if self.nested:
if self.nested_optimizer is None:
reward_h, reward_r = self._nested_rewards
reward_h = self.traj_h.reward(reward_h)
reward_r = self.traj.reward(reward_r)
self.nested_optimizer = utils.NestedMaximizer(reward_h, self.traj_h.u, reward_r, self.traj.u)
self.traj_h.x0.set_value(self.human.x)
self.nested_optimizer.maximize(bounds = self.bounds)
else:
print len(self.cache)
if self.index<len(self.cache):
self.u = self.cache[self.index]
else:
if self.simple_optimizer is None:
r = self.traj.reward(self._simple_reward)
self.simple_optimizer = utils.Maximizer(r, self.traj.u)
#TODO: make sure these bounds are correct, and that we shouldn't add bounded control to reward function
self.simple_optimizer.maximize(bounds = self.bounds)
self.cache.append(self.u)
self.index += 1
def reset(self):
Car.reset(self)
self.index = 0
self.cache = []
def platoon(self, front_car):
for i in range(0,100):
print('ASKING TO PLATOON')
self.platoon_behind = front_car