def sarsa(lambda_value, iteration_num):
"""
:rtype : MSE (float)
"""
print 'lambda:', lambda_value
# define functions (dictionaries)
action_value_function = ActionValue(float)
number_state = defaultdict(int)
number_state_action = defaultdict(int)
# define parameters
batch = 100
num_zero = 10
if lambda_value == 0. or lambda_value == 1.:
learning_curve = []
# iterate over iteration_num
for episode in xrange(iteration_num):
if episode % batch == 0:
print '\repisode:', episode,
if lambda_value == 0. or lambda_value == 1.:
learning_curve.append(calculate_mse(action_value_function))
# initialize state, action, epsilon, and eligibility-trace
state = environment.State()
current_dealer = state.dealer
current_player = state.player
epsilon = float(num_zero) / (num_zero + number_state[(current_dealer, current_player)])
current_action = epsilon_greedy(action_value_function, state, epsilon)
eligibility = defaultdict(int)
while state.terminal is False:
# count state and state_action number
number_state[(current_dealer, current_player)] += 1
number_state_action[(current_dealer, current_player, current_action)] += 1
# take an action
reward = step(state, current_action)
if reward is None:
# assign 0 if the match hasn't finished yet
reward = 0
new_dealer = state.dealer
new_player = state.player
# update epsilon
epsilon = float(num_zero) / (num_zero + number_state[(new_dealer, new_player)])
# decide an action
new_action = epsilon_greedy(action_value_function, state, epsilon)
# update alpha, delta, and eligibility-trace
alpha = 1. / number_state_action[(current_dealer, current_player, current_action)]
delta = reward + action_value_function[(new_dealer, new_player, new_action)] \
- action_value_function[(current_dealer, current_player, current_action)]
eligibility[(current_dealer, current_player, current_action)] += 1
# iterate over every state and action
# To Be Fixed: this implementation is probably slower than vectorized way
for key in action_value_function.keys():
dealer, player, action = key
# update the action value function
action_value_function[(dealer, player, action)] \
+= alpha * delta * eligibility[(dealer, player, action)]
# update eligibility-trace
eligibility[(dealer, player, action)] *= lambda_value
# update state and action
current_dealer = new_dealer
current_player = new_player
current_action = new_action
print '\repisode:', episode
print 'done!'
if lambda_value == 0. or lambda_value == 1.:
learning_curve.append(calculate_mse(action_value_function))
# plot learning curve
if lambda_value == 0. or lambda_value == 1.:
x = range(0, iteration_num + 1, batch)
pylab.title('Learning curve of Mean-Squared Error against episode number: lambda = ' + str(lambda_value))
pylab.xlabel("episode number")
pylab.xlim([0, iteration_num])
pylab.xticks(range(0, iteration_num + 1, batch))
pylab.ylabel("Mean-Squared Error")
pylab.plot(x, learning_curve)
pylab.show()
# calculate MSE
print 'calculate the Mean-Squared Error...'
MSE = calculate_mse(action_value_function)
## value function
#value_function = action_value_function.to_value_function()
## plot the optimal value function
#plot_value_function(value_function, "Optimal Value Function (Sarsa)")
return MSE
def sarsa(lambda_value, iteration_num):
"""
:rtype : MSE (float)
"""
print 'lambda:', lambda_value
# define functions (dictionaries)
action_value_function = ActionValueLinearApproximation(float)
linear_function = LinearFunction()
parameters_hit = np.array([0 for i in range(3 * 6)])
parameters_stick = np.array([0 for i in range(3 * 6)])
# define parameters
batch = 100
num_zero = 10
epsilon = 0.1
alpha = 0.01
HIT = 0
STICK = 1
if lambda_value == 0. or lambda_value == 1.:
learning_curve = []
# iterate over iteration_num
for episode in xrange(iteration_num):
if episode % batch == 0:
print '\repisode:', episode,
if lambda_value == 0. or lambda_value == 1.:
learning_curve.append(calculate_mse(action_value_function))
# initialize state, action, and eligibility-trace
state = environment.State()
linear_function.update(state)
current_features = linear_function.get_features()
action = epsilon_greedy_linear(action_value_function, current_features,
epsilon)
eligibility_hit = np.array([0 for i in range(3 * 6)])
eligibility_stick = np.array([0 for i in range(3 * 6)])
while state.terminal is False:
# update delta, and eligibility-trace
if action == HIT:
eligibility_hit = np.add(eligibility_hit,
np.array(current_features))
else:
eligibility_stick = np.add(eligibility_stick,
np.array(current_features))
# take an action
reward = step(state, action)
if reward is None:
# assign 0 if the match hasn't finished yet
reward = 0
linear_function.update(state)
new_features = linear_function.get_features()
# update delta
delta_hit = reward - np.array(new_features).dot(parameters_hit)
delta_stick = reward - np.array(new_features).dot(parameters_stick)
# update Action Value Function
if action == HIT:
action_value_function.update_value((new_features, action),
parameters_hit)
else:
action_value_function.update_value((new_features, action),
parameters_stick)
# update delta, parameters, and eligibility-trace
if action == HIT:
delta_hit += action_value_function[(new_features, HIT)]
else:
delta_stick += action_value_function[(new_features, STICK)]
parameters_hit = np.add(parameters_hit,
alpha * delta_hit * eligibility_hit)
parameters_stick = np.add(parameters_stick,
alpha * delta_stick * eligibility_stick)
eligibility_hit = eligibility_hit * lambda_value
eligibility_stick = eligibility_stick * lambda_value
# decide an action
action = epsilon_greedy_linear(action_value_function, new_features,
epsilon)
# update state and action
current_features = new_features
print '\repisode:', episode
print 'done!'
if lambda_value == 0. or lambda_value == 1.:
learning_curve.append(calculate_mse(action_value_function))
# plot learning curve
if lambda_value == 0. or lambda_value == 1.:
x = range(0, iteration_num + 1, batch)
pylab.title(
'Learning curve of Mean-Squared Error against episode number: lambda = '
+ str(lambda_value))
pylab.xlabel("episode number")
pylab.xlim([0, iteration_num])
pylab.xticks(range(0, iteration_num + 1, batch))
pylab.ylabel("Mean-Squared Error")
pylab.plot(x, learning_curve)
pylab.show()
# calculate MSE
print 'calculate the Mean-Squared Error...'
MSE = calculate_mse(action_value_function)
## value function
#value_function = action_value_function.to_value_function()
## plot the optimal value function
#plot_linear_value_function(action_value_function, "Optimal Value Function (Linear Approximation)")
return MSE
def sarsa(lambda_value, iteration_num):
"""
:rtype : MSE (float)
"""
print 'lambda:', lambda_value
# define functions (dictionaries)
action_value_function = ActionValueLinearApproximation(float)
linear_function = LinearFunction()
parameters_hit = np.array([0 for i in range(3 * 6)])
parameters_stick = np.array([0 for i in range(3 * 6)])
# define parameters
batch = 100
num_zero = 10
epsilon = 0.1
alpha = 0.01
HIT = 0
STICK = 1
if lambda_value == 0. or lambda_value == 1.:
learning_curve = []
# iterate over iteration_num
for episode in xrange(iteration_num):
if episode % batch == 0:
print '\repisode:', episode,
if lambda_value == 0. or lambda_value == 1.:
learning_curve.append(calculate_mse(action_value_function))
# initialize state, action, and eligibility-trace
state = environment.State()
linear_function.update(state)
current_features = linear_function.get_features()
action = epsilon_greedy_linear(action_value_function, current_features, epsilon)
eligibility_hit = np.array([0 for i in range(3 * 6)])
eligibility_stick = np.array([0 for i in range(3 * 6)])
while state.terminal is False:
# update delta, and eligibility-trace
if action == HIT:
eligibility_hit = np.add(eligibility_hit, np.array(current_features))
else:
eligibility_stick = np.add(eligibility_stick, np.array(current_features))
# take an action
reward = step(state, action)
if reward is None:
# assign 0 if the match hasn't finished yet
reward = 0
linear_function.update(state)
new_features = linear_function.get_features()
# update delta
delta_hit = reward - np.array(new_features).dot(parameters_hit)
delta_stick = reward - np.array(new_features).dot(parameters_stick)
# update Action Value Function
if action == HIT:
action_value_function.update_value((new_features, action), parameters_hit)
else:
action_value_function.update_value((new_features, action), parameters_stick)
# update delta, parameters, and eligibility-trace
if action == HIT:
delta_hit += action_value_function[(new_features, HIT)]
else:
delta_stick += action_value_function[(new_features, STICK)]
parameters_hit = np.add(parameters_hit, alpha * delta_hit * eligibility_hit)
parameters_stick = np.add(parameters_stick, alpha * delta_stick * eligibility_stick)
eligibility_hit = eligibility_hit * lambda_value
eligibility_stick = eligibility_stick * lambda_value
# decide an action
action = epsilon_greedy_linear(action_value_function, new_features, epsilon)
# update state and action
current_features = new_features
print '\repisode:', episode
print 'done!'
if lambda_value == 0. or lambda_value == 1.:
learning_curve.append(calculate_mse(action_value_function))
# plot learning curve
if lambda_value == 0. or lambda_value == 1.:
x = range(0, iteration_num + 1, batch)
pylab.title('Learning curve of Mean-Squared Error against episode number: lambda = ' + str(lambda_value))
pylab.xlabel("episode number")
pylab.xlim([0, iteration_num])
pylab.xticks(range(0, iteration_num + 1, batch))
pylab.ylabel("Mean-Squared Error")
pylab.plot(x, learning_curve)
pylab.show()
# calculate MSE
print 'calculate the Mean-Squared Error...'
MSE = calculate_mse(action_value_function)
## value function
#value_function = action_value_function.to_value_function()
## plot the optimal value function
#plot_linear_value_function(action_value_function, "Optimal Value Function (Linear Approximation)")
return MSE
dealer = state.dealer
player = state.player
# update epsilon
epsilon = float(num_zero) / (num_zero +
number_state[(dealer, player)])
# decide an action
action = epsilon_greedy(action_value_function, state, epsilon)
# count state and state_action number
number_state[(dealer, player)] += 1
number_state_action[(dealer, player, action)] += 1
# take an action
reward = step(state, action)
if reward is None:
# assign 0 if the match hasn't finished yet
reward = 0
# update the action value function
action_value_function[(dealer, player, action)] += \
(1. / number_state_action[(dealer, player, action)]) \
* (reward - action_value_function[(dealer, player, action)])
print '\repisode:', episode
print 'done!'
# calculate value function
value_function = action_value_function.to_value_function()
while state.terminal is False:
dealer = state.dealer
player = state.player
# update epsilon
epsilon = float(num_zero) / (num_zero + number_state[(dealer, player)])
# decide an action
action = epsilon_greedy(action_value_function, state, epsilon)
# count state and state_action number
number_state[(dealer, player)] += 1
number_state_action[(dealer, player, action)] += 1
# take an action
reward = step(state, action)
if reward is None:
# assign 0 if the match hasn't finished yet
reward = 0
# update the action value function
action_value_function[(dealer, player, action)] += \
(1. / number_state_action[(dealer, player, action)]) \
* (reward - action_value_function[(dealer, player, action)])
print '\repisode:', episode
print 'done!'
# calculate value function
value_function = action_value_function.to_value_function()
