def grid_sampling(theta, cm, K, Ke, N, epsilon):
theta_list = np.random.multivariate_normal(theta, cm, K)
result_list = []
for x in range(K):
# concurrent_eval(theta_list, x, result_list, N)
avg_reward = 0
for i in range(N):
grid = Grid()
grid.pi_params = theta_list[x].reshape(23, 4)
grid.softmax()
epi = GridEpisode(grid)
avg_reward += epi.run_all_steps()
result_list.append((theta_list[x], avg_reward / N))
# print(sorted(result_list, key=lambda n: n[-1], reverse=True))
elite_list = sorted(result_list, key=lambda n: n[-1], reverse=True)[:Ke]
# print(elite_list)
theta_final = np.zeros(92)
cm_final = epsilon * np.identity(92)
J_final = 0
for t in elite_list:
theta_final += t[0]
cm_final += np.array([t[0] - theta]).T.dot(np.array([t[0] - theta]))
J_final += t[1]
theta_final /= Ke
cm_final /= (epsilon + Ke)
# print(cm_final)
J_final /= Ke
return theta_final, cm_final, J_final
def qlearning_grid(lr, eps, epoch=100, searchbound=400):
grid = Grid()
grid.pi_params = np.zeros((23, 4))
grid.softmax()
actions = grid.action
estimated_rewards = np.zeros(epoch)
q = np.zeros((23, 4))
for x in range(epoch):
s = grid.d_zero()
while s != [5, 5]:
# choose new_a from new_s using policy derived from q
pi_temp = pe.softmax(q[grid.get_index(s)], actions, eps(x))
a = np.random.choice(actions, 1, p=pi_temp)[0]
# print(q)
# Take action a and observe r and s′;
new_s, r = grid.P_and_R(s, a)
q[grid.get_index(s), actions.index(a)] += lr * (
r + grid.gamma * np.max(q[grid.get_index(new_s)]) -
q[grid.get_index(s), actions.index(a)])
s = new_s
# using q function to estimate the reward and add it to estimated_reward
# print('episode: ', x, ', q function: ', q)
grid.pi_params = pe.softmax(q, actions, eps(x))
grid_epi = GridEpisode(grid, step_bound=searchbound)
# print('episode: ', x, ', pi: ', grid.pi_params)
estimated_rewards[x] = grid_epi.run_all_steps()
print('episode: ', x, ', reward: ', estimated_rewards[x], 'epsilon: ',
eps(x))
# decay *= decay_rate
return estimated_rewards
def grid_evaluate(table, N):
avg_reward = 0
for i in range(N):
g = Grid()
g.pi_params = table
g.softmax()
epi = GridEpisode(g)
avg_reward += epi.run_all_steps()
return avg_reward / N
def multi_grid_episode(table, l):
# total_reward = 0
for i in l:
grid = Grid()
# print(i)
grid.pi_params = table
grid.softmax()
epi = GridEpisode(grid)
grid_q.put(epi.run_all_steps())
return 0
def grid_evaluate(t, N):
reward_l = []
table = t.reshape(23, 4)
for i in range(N):
# concurrent_eval(theta_list, x, result_list, N)
grid = Grid()
# print(i)
grid.pi_params = table
grid.softmax()
epi = GridEpisode(grid)
reward_l.append(epi.run_all_steps())
return sum(reward_l) / N
def sarsa_lambda_grid(lr, l, eps, epoch=100, searchbound=400):
grid = Grid()
grid.pi_params = np.zeros((23, 4))
grid.softmax()
actions = grid.action
estimated_rewards = np.zeros(epoch)
# Initialize tabular-q arbitrarily
q = np.zeros((23, 4))
# for each episode:
for x in range(epoch):
# s ∼ d0
s = grid.d_zero()
# e ← 0
e = np.zeros((23, 4))
# choose a from s using a policy derived from q (e.g., ε-greedy or softmax);
pi_s = estimation.epsilon_greedy(q[grid.get_index(s)], actions, eps(x))
a = np.random.choice(actions, 1, p=pi_s)[0]
# for each time step, until s is the terminal absorbing state do
while s != [5, 5]:
# Take action a and observe r and s′;
new_s, r = grid.P_and_R(s, a)
# choose new_a from new_s using policy derived from q
pi_temp = estimation.epsilon_greedy(q[grid.get_index(new_s)], actions, eps(x))
new_a = np.random.choice(actions, 1, p=pi_temp)[0]
# e ← γλe + ∂qw(s,a)/∂qw;
e = l * grid.gamma * e
e[grid.get_index(s), actions.index(a)] += 1
# δ ← r + γqw(s′,a′) − qw(s,a);
delta = r + grid.gamma * q[grid.get_index(new_s), actions.index(new_a)] - q[grid.get_index(s), actions.index(a)]
# w ← w + αδe;
q += lr * delta * e
s = new_s
a = new_a
# using q function to estimate the reward and add it to estimated_reward
# print('episode: ', x, ', q function: ', q)
grid.pi_params = estimation.epsilon_greedy(q, actions, eps(x))
grid_epi = GridEpisode(grid, step_bound=searchbound)
# print('episode: ', x, ', pi: ', grid.pi_params)
estimated_rewards[x] = grid_epi.run_all_steps()
print('episode: ', x, ', reward: ', estimated_rewards[x], 'epsilon: ', eps(x))
# decay *= decay_rate
return estimated_rewards
def reinforce_grid(lr, eps, epoch=100, searchbound=400):
estimated_rewards = np.zeros(epoch)
# theta is a representation of policy
theta = np.zeros((23, 4))
grid = Grid()
actions = grid.action
# print(epoch)
# for each episode:
for x in range(epoch):
# s ∼ d0
s = grid.d_zero()
count = 0
hist_s = []
hist_a = []
hist_r = []
grid.pi_params = estimation.softmax(theta, eps(x))
# for each time step, until s is the terminal absorbing state do
while s != [5, 5] and count < 1000:
hist_s.append(s)
a = grid.pi(s)
hist_a.append(a)
new_s, r = grid.P_and_R(s, a)
hist_r.append(r)
s = new_s
count += 1
# delta_j = 0
decay = 1
for i in range(len(hist_s)):
g = 0
gd = 1
for j in range(i, len(hist_s)):
g += gd * hist_r[j]
gd *= grid.gamma
theta[grid.get_index(hist_s[i]),
actions.index(hist_a[i])] += lr * decay * g
decay *= grid.gamma
grid.pi_params = estimation.softmax(theta, eps(x))
# grid.softmax()
grid_epi = GridEpisode(grid, step_bound=searchbound)
# print('episode: ', x, ', pi: ', grid.pi_params)
estimated_rewards[x] = grid_epi.run_all_steps()
if x == epoch - 1:
print('episode: ', x, ', reward: ', estimated_rewards[x])
# decay *= decay_rate
return estimated_rewards
def actor_critic_grid(lr, eps, epoch=100, searchbound=400):
estimated_rewards = np.zeros(epoch)
# Initialize tabular-v arbitrarily
v = np.zeros(23)
# theta is a representation of policy
theta = np.zeros((23, 4))
grid = Grid()
actions = grid.action
# for each episode:
for x in range(epoch):
# s ∼ d0
s = grid.d_zero()
count = 0
# for each time step, until s is the terminal absorbing state do
while s != [5, 5] and count < 1000:
# a ∼ π(s, ·);
grid.pi_params = estimation.softmax(theta, eps(x))
a = grid.pi(s)
# Take action a and observe r and s′;
new_s, r = grid.P_and_R(s, a)
# Critic update using TD(λ)
# e ← γλe + ∂qw(s,a)/∂qw;
delta = r + grid.gamma * v[grid.get_index(new_s)] - v[
grid.get_index(s)]
# w←w+αδev;
v[grid.get_index(s)] += lr * delta
theta[grid.get_index(s), actions.index(a)] += lr * delta
# print(theta)
s = new_s
count += 1
# using q function to estimate the reward and add it to estimated_reward
# print('episode: ', x, ', q function: ', q)
grid.pi_params = estimation.softmax(theta, eps(x))
# grid.softmax()
grid_epi = GridEpisode(grid, step_bound=searchbound)
# print('episode: ', x, ', pi: ', grid.pi_params)
estimated_rewards[x] = grid_epi.run_all_steps()
if x == 99:
print('episode: ', x, ', reward: ', estimated_rewards[x])
# decay *= decay_rate
return estimated_rewards
for i in range(N):
cartpole = CartPole()
cartpole.pi_params = table
epi = CartPoleEpisode(cartpole)
avg_reward += epi.run_all_steps()
return avg_reward / N
tic = time.time()
theta = np.ones(92) * 0.25
theta_f = grid_param_sampling(theta, 0.5, 200)
grid = Grid()
grid.pi_params = theta_f.reshape(23, 4)
grid.softmax()
episode = GridEpisode(grid)
print('optimized reward: ', episode.run_all_steps())
print('optimized theta: ', theta_f.reshape(23, 4))
# theta = np.ones(8) * 0.25
# theta_f = cartpole_sampling(theta, 0.5, 500)
# cartpole = CartPole()
# cartpole.pi_params = theta_f.reshape(4, 2)
# episode = CartPoleEpisode(cartpole)
# print('optimized reward: ', episode.run_all_steps())
# print('optimized theta: ', theta_f.reshape(4, 2))
toc = time.time()
print('running time: ', (toc - tic) / 60, ' mins')