class FriendQLearner:
def __init__(self,
num_states=(2 * 4) * (2 * 4) * 2,
num_actions=5,
alpha=0.05,
alpha_decay=0.99999,
gamma=0.9,
epsilon=1.0,
max_steps_per_episode=1000,
max_num_episodes=10000000,
save_model_per=1000000,
verbose=True):
self.world = World()
self.game = Play_game()
# inputs
self.alpha = alpha
self.alpha_decay = alpha_decay
self.gamma = gamma
self.epsilon = epsilon
self.max_steps_per_episode = max_steps_per_episode
self.max_num_episodes = max_num_episodes
self.save_model_per = save_model_per
# initialize
self.num_states = num_states # 4x2 grid, two players, with or without ball
self.num_actions = num_actions
q_table = np.full(shape=(num_states, num_actions, num_actions),
fill_value=0.0)
self.q_tables = {'A': deepcopy(q_table), 'B': deepcopy(q_table)}
self.state = {}
# error
self.ERRs = []
self.steps_to_plot = []
self.verbose = verbose
def friendQ_agent(self):
self.step_count = 1
for episode in range(self.max_num_episodes):
# reset game
self.all_states, self.state, _ = self.game.init_game()
# play game and learn
for t in range(self.max_steps_per_episode):
# get actions
current_actions = self.get_actions()
# observe
state_prime, r, done, _ = self.game.play(current_actions)
# update
self.update_Q(current_actions, state_prime, r['A'], 'A')
self.update_Q(current_actions, state_prime, r['B'], 'B')
self.state = state_prime
self.step_count += 1
# save and plot
if self.step_count > 0 and self.step_count % self.save_model_per == 0:
experiment_id = 1
self.save_data(experiment_id)
self.plot_error(experiment_id)
if done:
self.alpha *= self.alpha_decay
break
if self.verbose:
print('episode: ', episode)
if self.step_count > 1000000:
break
def get_actions(self):
# off-policy
actions = {}
actions['A'] = rand.randint(0, self.num_actions - 1)
actions['B'] = rand.randint(0, self.num_actions - 1)
# if t == 0:
# self.actions = actions
return actions
def update_Q(self, actions, state_prime, r, player_name):
state_index = self.all_states[self.state]
state_prime_index = self.all_states[state_prime]
# other_player = set(['A', [B]]) - set(player_name)
# print other_player
# update Q table
V = np.amax(self.q_tables[player_name][state_prime_index])
# error = (((1 - self.gamma) * r + self.gamma * V) - self.q_tables[player_name][state_index, actions['A'], actions['B']]) * self.alpha
error = ((r + self.gamma * V) -
self.q_tables[player_name][state_index, actions['A'],
actions['B']]) * self.alpha
self.q_tables[player_name][state_index, actions['A'],
actions['B']] += error
# collect errors
if player_name == 'A' and self.state == 'B21' and actions[
'A'] == 1 and actions['B'] == 4:
self.ERRs.append(abs(error))
self.steps_to_plot.append(self.step_count)
# print self.ERRs
if self.verbose:
print('Action of B at state s: ',
np.argmax(self.q_tables['B'][self.all_states['B21']]) % 5)
def plot_error(self, experiment_id):
err_to_plot = self.ERRs[::20]
step_to_plot = self.steps_to_plot[::20]
plt.plot(step_to_plot, err_to_plot, '-', linewidth=1.3)
plt.ylim(0, 0.5)
plt.xlim(0, 1000000)
plt.xlabel("Simulation Iteration")
plt.ylabel("Q-value Difference")
plt.title("Friend-Q")
plt.savefig('outputs/FriendQ_exp_' + str(experiment_id) + '_' +
str(self.step_count) + '.png')
plt.show(block=False)
plt.show()
def save_data(self, experiment_id):
error_file_name = 'outputs/data_FriendQ_error_exp_' + str(
experiment_id) + '.txt'
error_file = open(error_file_name, 'w')
for item in self.ERRs:
error_file.write("%s\n" % item)
# step_file = open('step_test.txt', 'w')
step_file_name = 'outputs/data_FriendQ_step_exp_' + str(
experiment_id) + '.txt'
step_file = open(step_file_name, 'w')
for item in self.steps_to_plot:
step_file.write("%s\n" % item)
if self.verbose:
print('epsilon:', self.epsilon)
print('alpha: ', self.alpha)
class CeQLearner:
def __init__(
self,
num_states=(2 * 4) * (2 * 4) * 2,
num_actions=5,
alpha=1.0,
alpha_decay=0.99997,
gamma=0.9,
epsilon=1.0,
max_steps_per_episode=1000, #1000
max_num_episodes=10000000, #10000000
save_model_per=10000,
verbose=True):
self.world = World()
self.game = Play_game()
# inputs
self.alpha = alpha
self.alpha_decay = alpha_decay
self.gamma = gamma
self.epsilon = epsilon
self.max_steps_per_episode = max_steps_per_episode
self.max_num_episodes = max_num_episodes
self.save_model_per = save_model_per
# initialize
self.num_states = num_states # 4x2 grid, two players, with or without ball
self.num_actions = num_actions
q_table = np.full(shape=(num_states, num_actions, num_actions),
fill_value=1.0)
self.q_tables = {'A': deepcopy(q_table), 'B': deepcopy(q_table)}
self.state = {}
# error
self.ERRs = []
self.steps_to_plot = []
self.verbose = verbose
def ceQ_agent(self):
self.step_count = 1
for episode in range(self.max_num_episodes):
# reset game
self.all_states, self.state, _ = self.game.init_game()
# play game and learn
for t in range(self.max_steps_per_episode):
# get actions
current_actions = self.get_actions()
# observe
state_prime, r, done, _ = self.game.play(current_actions)
# update
self.update_Q(current_actions, state_prime, r['A'], r['B'])
self.state = state_prime
self.step_count += 1
# save and plot
if self.step_count > 0 and self.step_count % self.save_model_per == 0:
experiment_id = 1
self.save_data(experiment_id)
self.plot_error(experiment_id)
if done:
self.alpha *= self.alpha_decay
break
if self.step_count > 1000000:
break
def get_actions(self):
# off-policy
actions = {}
actions['A'] = rand.randint(0, self.num_actions - 1)
actions['B'] = rand.randint(0, self.num_actions - 1)
return actions
def get_V(self, q_table_0, q_table_1, state_prime):
num_states, num_action_0, num_action_1 = q_table_0.shape
q_table_0 = q_table_0[state_prime]
q_table_1 = q_table_1[state_prime]
c = -(q_table_0.flatten() + q_table_1.flatten())
A_ub = []
for i in range(num_action_0):
for j in range(num_action_0):
if i != j:
A_ub.append(
np.vstack([
np.zeros((i, num_action_1)),
q_table_0[j, :] - q_table_0[i, :],
np.zeros((num_action_0 - i - 1, num_action_1))
]).flatten())
for i in range(num_action_1):
for j in range(num_action_1):
if i != j:
A_ub.append(
np.vstack([
np.zeros((i, num_action_0)),
q_table_1[:, j] - q_table_1[:, i],
np.zeros((num_action_1 - i - 1, num_action_0))
]).transpose().flatten())
A_ub = np.stack(A_ub, 0)
b_ub = np.zeros([A_ub.shape[0]])
A_eq = [[1.0] * num_action_0 * num_action_1]
b_eq = [1.0]
bounds = [[0.0, None]] * num_action_0 * num_action_1
res = linprog(c,
A_ub=A_ub,
b_ub=b_ub,
A_eq=A_eq,
b_eq=b_eq,
bounds=bounds)
return res
def update_Q(self, actions, state_prime, r_A, r_B):
state_index = self.all_states[self.state]
state_prime_index = self.all_states[state_prime]
# update Q table
# V = np.amax(self.q_tables[player_name][state_prime_index])
res = self.get_V(self.q_tables['A'], self.q_tables['B'],
state_prime_index)
if res.success:
V = -res.fun
error_A = (((1 - self.gamma) * r_A + self.gamma * V) -
self.q_tables['A'][state_index, actions['A'],
actions['B']]) * self.alpha
error_B = (((1 - self.gamma) * r_B + self.gamma * V) -
self.q_tables['B'][state_index, actions['A'],
actions['B']]) * self.alpha
else:
error_A = 0.
error_B = 0.
self.q_tables['A'][state_index, actions['A'], actions['B']] += error_A
self.q_tables['B'][state_index, actions['A'], actions['B']] += error_B
# collect errors
if self.state == 'B21' and actions['A'] == 1 and actions['B'] == 4:
self.ERRs.append(abs(error_A))
self.steps_to_plot.append(self.step_count)
# print self.ERRs
# if self.verbose:
# print 'Action of B at state s: ', np.argmax(self.q_tables['B'][self.all_states['B21']]) % 5
def plot_error(self, experiment_id):
err_to_plot = self.ERRs
step_to_plot = self.steps_to_plot
plt.plot(step_to_plot, err_to_plot, '-', linewidth=0.8)
plt.ylim(0, 0.5)
# plt.xlim(0, 1000000)
plt.xlabel("Simulation Iteration")
plt.ylabel("Q-value Difference")
plt.title("Ce-Q")
plt.savefig('outputs/CeQ_exp_' + str(experiment_id) + '_' +
str(self.step_count) + '.png')
plt.show(block=False)
plt.show()
def save_data(self, experiment_id):
error_file_name = 'outputs/data_CeQ_error_exp_' + str(
experiment_id) + '.txt'
error_file = open(error_file_name, 'w')
for item in self.ERRs:
error_file.write("%s\n" % item)
step_file_name = 'outputs/data_CeQ_step_exp_' + str(
experiment_id) + '.txt'
step_file = open(step_file_name, 'w')
for item in self.steps_to_plot:
step_file.write("%s\n" % item)
if self.verbose:
print(self.ERRs)
print('epsilon:', self.epsilon)
print('alpha: ', self.alpha)
class QLearner:
def __init__(self,
num_states=(2 * 4) * (2 * 4) * 2,
num_actions=5,
alpha=1.0,
alpha_decay=0.99996,
gamma=0.9,
epsilon=1.0,
epsilon_decay=0.99996,
max_steps_per_episode=1000,
max_num_episodes=1000000,
save_model_per=1000000,
verbose=False):
self.world = World()
self.game = Play_game()
# inputs
self.alpha = alpha
self.alpha_decay = alpha_decay
self.gamma = gamma
self.epsilon = epsilon
self.epsilon_decay = epsilon_decay
self.max_steps_per_episode = max_steps_per_episode
self.max_num_episodes = max_num_episodes
self.save_model_per = save_model_per
# initialize
self.num_states = num_states # 4x2 grid, two players, with or without ball
self.num_actions = num_actions
self.q_table = np.full(shape=(num_states, num_actions), fill_value=1.0)
self.q_tables = {
'A': deepcopy(self.q_table),
'B': deepcopy(self.q_table)
}
self.state = {}
self.actions = {
'A': 0,
'B': 0
} # map N, S, E, W, and stick to [0,1,2,3,4]
# error
self.ERRs = []
self.steps_to_plot = []
self.verbose = verbose
def Qlearning_agent(self, verbose):
self.step_count = 1
for episode in range(self.max_num_episodes):
# reset game
self.all_states, self.state, _ = self.game.init_game()
self.actions = self.get_first_actions(self.state)
# play game and learn
for t in range(self.max_steps_per_episode):
# update q_table seperately
state_prime, r, done, _ = self.game.play(self.actions)
self.actions['A'] = self.update_Q(state_prime, r['A'], 'A')
self.actions['B'] = self.update_Q(state_prime, r['B'], 'B')
self.state = state_prime
self.step_count += 1
# save and plot
if self.step_count > 0 and self.step_count % self.save_model_per == 0:
experiment_id = 1
self.save_data(experiment_id)
self.plot_error(experiment_id)
if done:
self.epsilon *= self.epsilon_decay
self.alpha *= self.alpha_decay
break
if self.step_count > 1000000:
break
def get_first_actions(self, s):
first_actions = {}
first_actions['A'] = rand.randint(0, self.num_actions - 1)
first_actions['B'] = rand.randint(0, self.num_actions - 1)
if self.verbose: print('s =', s, 'a =', first_actions)
return first_actions
def update_Q(self, state_prime, r, player_name):
# epsilon-greedy selection function
state_index = self.all_states[self.state]
state_prime_index = self.all_states[state_prime]
if rand.random() < self.epsilon:
action = rand.randint(0, self.num_actions - 1)
else:
action = np.argmax(self.q_tables[player_name][state_prime_index])
# update Q table
V = np.amax(self.q_tables[player_name][state_prime_index])
error = (
((1 - self.gamma) * r + self.gamma * V) -
self.q_tables[player_name][state_index,
self.actions[player_name]]) * self.alpha
self.q_tables[player_name][state_index,
self.actions[player_name]] += error
# collect errors
if player_name == 'A' and self.state == 'B21' and self.actions[
'A'] == 1 and error != 0.0:
self.ERRs.append(abs(error))
self.steps_to_plot.append(self.step_count)
# print self.ERRs
if self.verbose: print('s =', state_prime, 'a =', action, 'r =', r)
return action
def plot_error(self, experiment_id):
err_to_plot = self.ERRs[::20]
step_to_plot = self.steps_to_plot[::20]
plt.plot(step_to_plot, err_to_plot, '-', linewidth=0.3)
plt.ylim(0, 0.5)
plt.xlim(0, 1000000)
plt.xlabel("Simulation Iteration")
plt.ylabel("Q-value Difference")
plt.title("Q-learner")
plt.savefig('outputs/Q_exp_' + str(experiment_id) + '_' +
str(self.step_count) + '.png')
plt.show(block=False)
plt.show()
def save_data(self, experiment_id):
error_file_name = 'outputs/data_Q_error_exp_' + str(
experiment_id) + '.txt'
error_file = open(error_file_name, 'w')
for item in self.ERRs:
error_file.write("%s\n" % item)
# step_file = open('step_test.txt', 'w')
step_file_name = 'outputs/data_Q_step_exp_' + str(
experiment_id) + '.txt'
step_file = open(step_file_name, 'w')
for item in self.steps_to_plot:
step_file.write("%s\n" % item)
if self.verbose:
print(self.ERRs)
print('epsilon:', self.epsilon)
print('alpha: ', self.alpha)
class FoeQLearner:
def __init__(self,
num_states=(2 * 4) * (2 * 4) * 2,
num_actions=5,
alpha=1.0,
alpha_decay=0.99997,
gamma=0.9,
epsilon=1.0,
max_steps_per_episode=1000,
max_num_episodes=1000000,
save_model_per=20000,
verbose=True):
self.world = World()
self.game = Play_game()
# inputs
self.alpha = alpha
self.alpha_decay = alpha_decay
self.gamma = gamma
self.epsilon = epsilon
self.max_steps_per_episode = max_steps_per_episode
self.max_num_episodes = max_num_episodes
self.save_model_per = save_model_per
# initialize
self.num_states = num_states # 4x2 grid, two players, with or without ball
self.num_actions = num_actions
q_table = np.full(shape=(num_states, num_actions, num_actions),
fill_value=1.0)
self.q_tables = {'A': deepcopy(q_table), 'B': deepcopy(q_table)}
self.state = {}
# error
self.ERRs = []
self.steps_to_plot = []
self.verbose = verbose
def foeQ_agent(self):
self.step_count = 1
for episode in range(self.max_num_episodes):
# reset game
self.all_states, self.state, _ = self.game.init_game()
# play game and learn
for t in range(self.max_steps_per_episode):
# get actions
current_actions = self.get_actions()
# observe
state_prime, r, done, _ = self.game.play(current_actions)
# update
self.update_Q(current_actions, state_prime, r['A'], 'A')
self.update_Q(current_actions, state_prime, r['B'], 'B')
self.state = state_prime
self.step_count += 1
# save and plot
if self.step_count > 0 and self.step_count % self.save_model_per == 0:
experiment_id = 3
self.save_data(experiment_id)
self.plot_error(experiment_id)
if done:
self.alpha *= self.alpha_decay
break
if self.step_count > 1000000:
break
def get_actions(self):
# off-policy
actions = {}
actions['A'] = rand.randint(0, self.num_actions - 1)
actions['B'] = rand.randint(0, self.num_actions - 1)
# if t == 0:
# self.actions = actions
return actions
def get_V(self, q_table, state_prime):
num_states, num_action_0, num_action_1 = q_table.shape
# -1.0 because we need to maximize
c = [-1.0] + [0.0] * num_action_0
A_ub = np.transpose(
np.concatenate([[[1.0] * num_action_0], -q_table[state_prime]], 0))
b_ub = [0.0] * num_action_1
A_eq = [[0.0] + [1.0] * num_action_0]
b_eq = [1.0]
bounds = [[None, None]] + [[0.0, None]] * num_action_0
res = linprog(c,
A_ub=A_ub,
b_ub=b_ub,
A_eq=A_eq,
b_eq=b_eq,
bounds=bounds)
V = res.x[0] if type(res.x) != float else 1.0
# V = res.x[0]
return V
def update_Q(self, actions, state_prime, r, player_name):
state_index = self.all_states[self.state]
state_prime_index = self.all_states[state_prime]
# update Q table
# V = np.amax(self.q_tables[player_name][state_prime_index])
V = self.get_V(self.q_tables[player_name], state_prime_index)
error = (((1 - self.gamma) * r + self.gamma * V) -
self.q_tables[player_name][state_index, actions['A'],
actions['B']]) * self.alpha
self.q_tables[player_name][state_index, actions['A'],
actions['B']] += error
# collect errors
if player_name == 'A' and self.state == 'B21' and actions[
'A'] == 1 and actions['B'] == 4:
self.ERRs.append(abs(error))
self.steps_to_plot.append(self.step_count)
# print self.ERRs
# if self.verbose:
# print 'Action of B at state s: ', np.argmax(self.q_tables['B'][self.all_states['B21']]) % 5
def plot_error(self, experiment_id):
err_to_plot = self.ERRs
step_to_plot = self.steps_to_plot
plt.plot(step_to_plot, err_to_plot, '-', linewidth=0.5)
plt.ylim(0, 0.5)
plt.xlim(0, 1000000)
plt.xlabel("Simulation Iteration")
plt.ylabel("Q-value Difference")
plt.title("Foe-Q")
plt.savefig('outputs/FoeQ_exp_' + str(experiment_id) + '_' +
str(self.step_count) + '.png')
plt.show(block=False)
plt.show()
def save_data(self, experiment_id):
error_file_name = 'outputs/data_FeoQ_error_exp_' + str(
experiment_id) + '.txt'
error_file = open(error_file_name, 'w')
for item in self.ERRs:
error_file.write("%s\n" % item)
# step_file = open('step_test.txt', 'w')
step_file_name = 'outputs/data_FoeQ_step_exp_' + str(
experiment_id) + '.txt'
step_file = open(step_file_name, 'w')
for item in self.steps_to_plot:
step_file.write("%s\n" % item)
if self.verbose:
print(self.ERRs)
print('epsilon:', self.epsilon)
print('alpha: ', self.alpha)