sigma = d['sigma']
b = d['b']
r = d['r']
lrz = Lorenz(sigma, b, r)
lrz.X = d['X']
lrz.U = d['U']
"""
check robustness of trained control
generates n trajectories and store their start and end state
if the end state lies within a ball of radius 0.14 of the desired state,
control objective is achieved and documented as a 1 in the task. otherwise it is documented as 0.
sum of all the tasks divided by number of tasks gives the accuracy of the learning algorithm
"""
m = 1000 # number of trajectories to generate for checking accuracy of learned algorithm
n = 1000 # number of time steps for each trajectory
lrz.dt = 0.01 # set default time step to 0.01
xstart = np.zeros((m, 3)) # stores the initial state of the n trajectories
xend = np.zeros((m, 3)) # stores the final state of the n trajectories
task = np.zeros(
(m, 1), dtype=float
) # for each trajectory task stores 1 (resp. 0) for control objective
# (resp. not) achieved
for j in range(m):
xstart[j, :] = lrz.reset()
lrz.trajectory(n, 0)
xend[j, :] = lrz.state
if lrz.reward() > -0.15:
task[j, 0] = 1.0
print('Efficiency of the learning algorithm is ',
