def td_cp_single(f_order, alpha):
d = 4
cartpole = CartPole()
print('cartpole ', f_order, ' td')
weight = np.zeros((1, (f_order + 1) ** d))
# update weight in 100 loops
print('alpha = ', alpha)
for x in range(100):
s = cartpole.d_zero()
count = 0
while np.abs(s[0]) < cartpole.edge and np.abs(s[1]) < cartpole.fail_angle and count < 1010:
a = cartpole.pi(s)
new_s, r = cartpole.P_and_R(s, a)
weight += alpha * (r + vw(weight, new_s, f_order) - vw(weight, s, f_order)) * dvwdw(weight, s,
f_order).T
s = new_s
print(weight)
count += 1
# calculate td in another 100 loops
td_list = []
for x in range(100):
s = cartpole.d_zero()
count = 0
while np.abs(s[0]) < cartpole.edge and np.abs(s[1]) < cartpole.fail_angle and count < 1010:
a = cartpole.pi(s)
new_s, r = cartpole.P_and_R(s, a)
td_list.append((r + vw(weight, new_s, f_order) - vw(weight, s, f_order)) ** 2)
s = new_s
count += 1
td_list.append(0)
print('square td = ', np.mean(np.array(td_list)))
def sarsa_cartpole(lr, baseparams, epoch=100, eps=1e-2, base='fourier'):
cartpole = CartPole()
estimated_rewards = np.zeros(epoch)
actions = cartpole.actions
w = None
if base == 'fourier':
order = baseparams['order']
s = cartpole.d_zero()
w = np.zeros((1, len(actions) * (order + 1)**len(s)))
elif base == 'tile':
num_tilings, tiles_per_tiling = baseparams['num_tilings'], baseparams[
'tiles_per_tiling']
s = cartpole.d_zero()
w = np.zeros(
(1, len(actions) * num_tilings * (tiles_per_tiling**len(s))))
elif base == 'rbf':
order = baseparams['order']
s = cartpole.d_zero()
w = np.zeros((1, len(actions) * order**len(s)))
for x in range(epoch):
s = cartpole.d_zero()
# choose a from s using a policy derived from q (e.g., ε-greedy or softmax);
first_q = pe.epsilon_greedy(pe.qw(w, s, actions, base, baseparams),
actions, eps)
# pi_s = pe.epsilon_greedy(pe.qw(w, s, order, actions, base), actions, eps)
a = np.random.choice(actions, 1, p=first_q)[0]
count = 0
while np.abs(s[0]) < cartpole.edge and np.abs(
s[1]) < cartpole.fail_angle and count < 1010:
# Take action a and observe r and s′;
new_s, r = cartpole.P_and_R(s, a)
# Choose a′ from s′ using a policy derived from q;
pi_temp = pe.epsilon_greedy(
pe.qw(w, new_s, actions, base, baseparams), actions, eps)
new_a = np.random.choice(actions, 1, p=pi_temp)[0]
# w += lr * (r + pe.qw_fourier_ele(w, new_s, new_a, order, actions) -
# pe.qw_fourier_ele(w, s, a, order, actions)) * pe.dqwdw_fourier(s, a, order, actions)
new_q = pe.qw_ele(w, new_s, new_a, actions, base, baseparams)[0]
q, dqdw = pe.qw_ele(w, s, a, actions, base, baseparams)
w += lr * (r + new_q - q) * dqdw
s = new_s
a = new_a
count += 1
epi = CartPoleEpisode(cartpole)
estimated_rewards[x] = epi.run_with_w(w, eps, base, baseparams)
print('episode: ', x, ', reward: ', estimated_rewards[x])
# print('episode: ', x, ', w: ', w)
return estimated_rewards
def td_cp(lrs, f_order):
d = 4
alpha_result = []
cartpole = CartPole()
print('cartpole ', f_order, ' td')
# kth order Fourier Basis is defined as:
for alpha in lrs:
weight = np.zeros((1, (f_order + 1) ** d))
# update weight in 100 loops
print('alpha = ', alpha)
for x in range(100):
s = cartpole.d_zero()
count = 0
while np.abs(s[0]) < cartpole.edge and np.abs(s[1]) < cartpole.fail_angle and count < 1010:
a = cartpole.pi(s)
new_s, r = cartpole.P_and_R(s, a)
weight += alpha * (r + vw(weight, new_s, f_order) - vw(weight, s, f_order)) * dvwdw(weight, s,
f_order).T
s = new_s
count += 1
# print(weight)
# calculate td in another 100 loops
td_list = []
for x in range(100):
s = cartpole.d_zero()
count = 0
while np.abs(s[0]) < cartpole.edge and np.abs(s[1]) < cartpole.fail_angle and count < 1010:
a = cartpole.pi(s)
new_s, r = cartpole.P_and_R(s, a)
td_list.append((r + vw(weight, new_s, f_order) - vw(weight, s, f_order)) ** 2)
s = new_s
count += 1
td_list.append(0)
msv = np.mean(np.array(td_list))
print('square td = ', msv)
if np.isnan(msv):
alpha_result.append(1e100)
else:
alpha_result.append(msv)
print('##########################')
return alpha_result
def qlearning_cartpole(lr, baseparams, decaylambda, epoch=100, base='fourier'):
cartpole = CartPole()
estimated_rewards = np.zeros(epoch)
actions = cartpole.actions
w = None
if base == 'fourier':
order = baseparams['order']
s = cartpole.d_zero()
w = np.zeros((1, len(actions) * (order + 1)**len(s)))
elif base == 'tile':
num_tilings, tiles_per_tiling = baseparams['num_tilings'], baseparams[
'tiles_per_tiling']
s = cartpole.d_zero()
w = np.zeros((1, len(actions) * num_tilings))
for x in range(epoch):
s = cartpole.d_zero()
count = 0
while np.abs(s[0]) < cartpole.edge and np.abs(
s[1]) < cartpole.fail_angle and count < 1010:
# Choose a′ from s′ using a policy derived from q;
pi_temp = pe.epsilon_greedy(pe.qw(w, s, actions, base, baseparams),
actions, decaylambda(x))
a = np.random.choice(actions, 1, p=pi_temp)[0]
# Take action a and observe r and s′;
new_s, r = cartpole.P_and_R(s, a)
# w += lr * (r + pe.qw_fourier_ele(w, new_s, new_a, order, actions) -
# pe.qw_fourier_ele(w, s, a, order, actions)) * pe.dqwdw_fourier(s, a, order, actions)
new_q = np.max(pe.qw(w, new_s, actions, base, baseparams))
q, dqdw = pe.qw_ele(w, s, a, actions, base, baseparams)
w += lr * (r + new_q - q) * dqdw
s = new_s
count += 1
epi = CartPoleEpisode(cartpole)
estimated_rewards[x] = epi.run_with_w_softmax(w, decaylambda(x), base,
baseparams)
print('episode: ', x, ', reward: ', estimated_rewards[x])
# print('episode: ', x, ', w: ', w)
return estimated_rewards