def test_reward():
'''
Test reward function by comparing to reward.m
'''
k = 2 # state dim
m = np.random.rand(1, k)
s = np.random.rand(k, k)
s = s.dot(s.T)
reward = ExponentialReward(k)
W = reward.W.numpy()
t = reward.t.numpy()
M, S = reward.compute_reward(m, s)
M_mat, _, _, S_mat = octave.reward(m.T, s, t.T, W, nout=4)
np.testing.assert_allclose(M, M_mat)
np.testing.assert_allclose(S, S_mat)
def test_reward():
'''
Test reward function by comparing to reward.m
'''
k = 2 # state dim
m = np.random.rand(1, k)
s = np.random.randn(k, k)
s = s.dot(s.T)
reward = ExponentialReward(k)
W = reward.W.data.cpu().numpy()
t = reward.t.data.cpu().numpy()
M, S = reward.compute_reward(
torch.tensor(m).float().cuda(),
torch.tensor(s).float().cuda())
M_mat, _, _, S_mat = octave.reward(m.T, s, t.T, W, nout=4)
import pdb
pdb.set_trace()
np.testing.assert_allclose(M.cpu().numpy(), M_mat)
np.testing.assert_allclose(S.cpu().numpy(), S_mat)
for model in pilco.mgpr.models:
model.likelihood.variance.assign(0.001)
set_trainable(model.likelihood.variance, False)
r_new = np.zeros((T, 1))
for rollouts in range(N):
print("**** ITERATION no", rollouts, " ****")
pilco.optimize_models(maxiter=maxiter, restarts=2)
pilco.optimize_policy(maxiter=maxiter, restarts=2)
X_new, Y_new, _, _ = rollout(env,
pilco,
timesteps=T_sim,
verbose=True,
SUBS=SUBS,
render=True)
# Since we had decide on the various parameters of the reward function
# we might want to verify that it behaves as expected by inspection
for i in range(len(X_new)):
r_new[:, 0] = R.compute_reward(X_new[i, None, :-1],
0.001 * np.eye(state_dim))[0]
total_r = sum(r_new)
_, _, r = pilco.predict(X_new[0, None, :-1], 0.001 * S_init, T)
print("Total ", total_r, " Predicted: ", r)
# Update dataset
X = np.vstack((X, X_new))
Y = np.vstack((Y, Y_new))
pilco.mgpr.set_data((X, Y))
