我正在比较使用Python 3.7和Julia 1.2进行的小车杆模拟。在Python中，该模拟是作为类对象编写的，如下所示，在Julia中则只是一个函数。我发现使用Julia解决问题需要约0.2秒，这比Python慢得多。我不太了解Julia，也不知道具体原因是什么。我猜测与编译或循环设置有关。
import math
import random
from collections import namedtuple
RAD_PER_DEG = 0.0174533
DEG_PER_RAD = 57.2958
State = namedtuple('State', 'x x_dot theta theta_dot')
class CartPole:
""" Model for the dynamics of an inverted pendulum
"""
def __init__(self):
self.gravity = 9.8
self.masscart = 1.0
self.masspole = 0.1
self.length = 0.5 # actually half the pole's length
self.force_mag = 10.0
self.tau = 0.02 # seconds between state updates
self.x = 0
self.x_dot = 0
self.theta = 0
self.theta_dot = 0
@property
def state(self):
return State(self.x, self.x_dot, self.theta, self.theta_dot)
def reset(self, x=0, x_dot=0, theta=0, theta_dot=0):
""" Reset the model of a cartpole system to it's initial conditions
" theta is in radians
"""
self.x = x
self.x_dot = x_dot
self.theta = theta
self.theta_dot = theta_dot
def step(self, action):
""" Move the state of the cartpole simulation forward one time unit
"""
total_mass = self.masspole + self.masscart
pole_masslength = self.masspole * self.length
force = self.force_mag if action else -self.force_mag
costheta = math.cos(self.theta)
sintheta = math.sin(self.theta)
temp = (force + pole_masslength * self.theta_dot ** 2 * sintheta) / total_mass
# theta acceleration
theta_dotdot = (
(self.gravity * sintheta - costheta * temp)
/ (self.length *
(4.0/3.0 - self.masspole * costheta * costheta /
total_mass)))
# x acceleration
x_dotdot = temp - pole_masslength * theta_dotdot * costheta / total_mass
self.x += self.tau * self.x_dot
self.x_dot += self.tau * x_dotdot
self.theta += self.tau * self.theta_dot
self.theta_dot += self.tau * theta_dotdot
return self.state
运行模拟需要使用以下代码：
from cartpole import CartPole
import time
cp = CartPole()
start = time.time()
for i in range(100000):
cp.step(True)
end = time.time()
print(end-start)
这段代码是Julia语言编写的
function cartpole(state, action)
"""Cart and Pole simulation in discrete time
Inputs: cartpole( state, action )
state: 1X4 array [cart_position, cart_velocity, pole_angle, pole_velocity]
action: Boolean True or False where true is a positive force and False is a negative force
"""
gravity = 9.8
masscart = 1.0
masspole = 0.1
l = 0.5 # actually half the pole's length
force_mag = 10.0
tau = 0.02 # seconds between state updates
# x = 0
# x_dot = 0
# theta = 0
# theta_dot = 0
x = state[1]
x_dot = state[2]
theta = state[3]
theta_dot = state[4]
total_mass = masspole + masscart
pole_massl = masspole * l
if action == 0
force = force_mag
else
force = -force_mag
end
costheta = cos(theta)
sintheta = sin(theta)
temp = (force + pole_massl * theta_dot^2 * sintheta) / total_mass
# theta acceleration
theta_dotdot = (gravity * sintheta - costheta * temp)/ (l *(4.0/3.0 - masspole * costheta * costheta / total_mass))
# x acceleration
x_dotdot = temp - pole_massl * theta_dotdot * costheta / total_mass
x += tau * x_dot
x_dot += tau * x_dotdot
theta += tau * theta_dot
theta_dot += tau * theta_dotdot
new_state = [x x_dot theta theta_dot]
return new_state
end
代码如下：
@time include("cartpole.jl")
function run_sim()
"""Runs the cartpole simulation
No inputs or ouputs
Use with @time run_sim() for timing puposes.
"""
state = [0 0 0 0]
for i = 1:100000
state = cartpole( state, 0)
#print(state)
#print("\n")
end
end
@time run_sim()
0.171631秒，然后第二次运行代码时，时间为0.021331秒（即不包括编译时间）。相比之下，当我运行你的Python代码（尽管是在Python 3.6上）时，我得到了0.278秒的时间。不过，正如提到的那样，你还有许多进一步的性能优化可供选择。 - Mason