def compare_V(critic, A, B, Q, R, K, T, gamma, alpha, low=-1, high=1):
fig, ax = plt.subplots()
colors = ['#B53737', '#2D328F'] # red, blue
label_fontsize = 18
states = torch.linspace(low, high).detach().reshape(100, 1)
values = alpha * critic(states).squeeze().detach().numpy()
ax.plot(states.numpy(),
values,
color=colors[0],
label='Approx. Loss Function')
ax.plot(states.numpy(),
control.trueloss(A, B, Q, R, K, states.numpy(), T,
gamma).reshape(states.shape[0]),
color=colors[1],
label='Real Loss Function')
ax.set_xlabel('x', fontsize=label_fontsize)
ax.set_ylabel('y', fontsize=label_fontsize)
plt.legend()
plt.grid(True)
plt.show()
return
def live_train(K, low=-1, high=1):
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.set_xlim(low, high)
xtest = np.linspace(low, high, 100)
for j in range(NUM_TRIALS):
x = np.random.randn(1).reshape(1, 1)
for i in range(T):
if (i + 1) % (T / 5) == 0:
y_hat = np.array([
ALPHA * model(np.array(x1).reshape(1, 1)).item()
for x1 in xtest
])
xs = xtest.reshape(xtest.size, 1)
y = control.trueloss(A, B, Q, R, K, xs, T,
GAMMA).reshape(xtest.size)
ax.clear()
ax.plot(xtest, y_hat, 'r-')
ax.plot(xtest, y, 'k-')
plt.grid(True)
ax.set_xlabel('x', fontsize=18)
ax.set_ylabel('y', fontsize=18)
plt.pause(0.05)
u = -np.matmul(K, x)
r = np.matmul(x, np.matmul(Q, x)) + np.matmul(u, np.matmul(R, u))
y = r + ALPHA * GAMMA * model(np.matmul(A, x) + np.matmul(B, u))
y_hat = model.net_forward(x)
lr = 0.001
model.net_backward(y, y_hat, ALPHA)
model.update_wb(lr)
x = np.matmul(A, x) + np.matmul(B, u)
plt.show()
return