def caus_ent_backward(transition,reward_f,conv=5,discount = 0.9,z_states = None):
num_actions = transition.tot_actions;num_states = transition.tot_states
if reward_f.shape[0] ==num_actions:
state_action = True
else: state_action =False
z_actions = np.zeros([num_actions,num_states])
if z_states==None:
z_states = np.zeros(num_states)
#Backward - - - - - - - - - - - - - - - - - - - - - - - - - -
#print "Caus Ent Backward"
count = 0
delta = 0
while True:
prev = np.zeros(z_states.shape)
prev += z_states
for i in range(num_states):
tr = transition.dense_backward[i]
ch = transition.chunks_backward[i]
out = discount*np.array(sum_chunks(tr[2,:]*z_states[map(int,tr[1,:])],ch))
z_actions[:,i] = out +reward_f[:,i]
m = np.amax(z_actions,axis = 0)
z_states = m + np.log(np.sum(np.exp(z_actions-m),axis = 0))
count+=1
#Action Probability Computation - - - - - - - - - - - - - - - -
delta = np.amax(np.absolute(prev-z_states))
if count == 50:
#print "Count and delta", count,delta
z_actions = z_actions
m = np.amax(z_actions,axis = 0)
z_states = m + np.log(np.sum(np.exp(z_actions-m),axis = 0))
policy= np.exp(z_actions-z_states)
break
return policy,np.log(policy),z_states
def caus_ent_backward_nodisount(transition,reward_f,steps):
num_actions = transition.tot_actions;num_states = transition.tot_states
if reward_f.shape[0] ==num_actions:
state_action = True
else: state_action =False
gamma = discount
z_actions = np.zeros([num_actions,num_states])
z_states = np.zeros(num_states)
#Backward - - - - - - - - - - - - - - - - - - - - - - - - - -
print "Caus Ent Backward"
count = 0
delta = 0
for j in range(steps):
prev = np.zeros(z_states.shape)
prev += z_states
for i in range(num_states):
tr = transition.dense_backward[i]
ch = transition.chunks_backward[i]
out = gamma*np.array(sum_chunks(tr[2,:]*z_states[map(int,tr[1,:])],ch))
z_actions[:,i] = out +reward_f[:,i]
m = np.amax(z_actions)
z_states = m + np.log(np.sum(np.exp(z_actions-m),axis = 0))
count+=1
#Action Probability Computation - - - - - - - - - - - - - - - -
delta = np.sum(np.sum(np.absolute(prev-z_states)))
#delta +=1
#print "DElta cause",delta,delta2
if j==steps-1:
z_actions = z_actions
m = np.amax(z_actions)
z_states = m + np.log(np.sum(np.exp(z_actions-m),axis = 0))
policy= np.exp(z_actions-z_states)
return policy,np.log(policy),z_states
def timed_backward(transition, reward_f, conv=5, discount=0.9, z_states=None):
sweep_timer = timer()
convergence_timer = timer()
num_actions = transition.tot_actions
num_states = transition.tot_states
if reward_f.shape[0] == num_actions:
state_action = True
else:
state_action = False
gamma = discount
z_actions = np.zeros([num_actions, num_states])
if z_states == None:
z_states = np.zeros(num_states)
#Backward - - - - - - - - - - - - - - - - - - - - - - - - - -
print "Caus Ent Backward"
count = 0
delta = 0
convergence_timer.start()
while True:
prev = np.zeros(z_states.shape)
prev += z_states
sweep_timer.start()
for i in range(num_states):
tr = transition.dense_backward[i]
ch = transition.chunks_backward[i]
out = gamma * np.array(
sum_chunks(tr[2, :] * z_states[map(int, tr[1, :])], ch))
z_actions[:, i] = out + reward_f[:, i]
sweep_timer.stop()
m = np.amax(z_actions)
z_states = m + np.log(np.sum(np.exp(z_actions - m), axis=0))
count += 1
#Action Probability Computation - - - - - - - - - - - - - - - -
delta = np.amax(np.absolute(prev - z_states))
print delta
if count > 2 and delta < conv:
print "Count and delta", count, delta
z_actions = z_actions
m = np.amax(z_actions)
z_states = m + np.log(np.sum(np.exp(z_actions - m), axis=0))
policy = np.exp(z_actions - z_states)
break
convergence_timer.stop()
times = [
sum(convergence_timer.time_taken) / len(convergence_timer.time_taken),
sum(sweep_timer.time_taken) / len(sweep_timer.time_taken)
]
return policy, np.log(policy), z_states, times
def timed_backward(transition,reward_f,conv=5,discount = 0.9,z_states = None):
sweep_timer = timer()
convergence_timer = timer()
num_actions = transition.tot_actions;num_states = transition.tot_states
if reward_f.shape[0] ==num_actions:
state_action = True
else: state_action =False
gamma = discount
z_actions = np.zeros([num_actions,num_states])
if z_states==None:
z_states = np.zeros(num_states)
#Backward - - - - - - - - - - - - - - - - - - - - - - - - - -
print "Caus Ent Backward"
count = 0
delta = 0
convergence_timer.start()
while True:
prev = np.zeros(z_states.shape)
prev += z_states
sweep_timer.start()
for i in range(num_states):
tr = transition.dense_backward[i]
ch = transition.chunks_backward[i]
out = gamma*np.array(sum_chunks(tr[2,:]*z_states[map(int,tr[1,:])],ch))
z_actions[:,i] = out +reward_f[:,i]
sweep_timer.stop()
m = np.amax(z_actions)
z_states = m + np.log(np.sum(np.exp(z_actions-m),axis = 0))
count+=1
#Action Probability Computation - - - - - - - - - - - - - - - -
delta = np.amax(np.absolute(prev-z_states))
print delta
if count>2 and delta<conv:
print "Count and delta", count,delta
z_actions = z_actions
m = np.amax(z_actions)
z_states = m + np.log(np.sum(np.exp(z_actions-m),axis = 0))
policy= np.exp(z_actions-z_states)
break
convergence_timer.stop()
times = [sum(convergence_timer.time_taken)/len(convergence_timer.time_taken),sum(sweep_timer.time_taken)/len(sweep_timer.time_taken)]
return policy,np.log(policy),z_states,times
def caus_ent_backward(transition,
reward_f,
conv=5,
discount=0.9,
z_states=None):
num_actions = transition.tot_actions
num_states = transition.tot_states
if reward_f.shape[0] == num_actions:
state_action = True
else:
state_action = False
z_actions = np.zeros([num_actions, num_states])
if z_states == None:
z_states = np.zeros(num_states)
#Backward - - - - - - - - - - - - - - - - - - - - - - - - - -
#print "Caus Ent Backward"
count = 0
delta = 0
while True:
prev = np.zeros(z_states.shape)
prev += z_states
for i in range(num_states):
tr = transition.dense_backward[i]
ch = transition.chunks_backward[i]
out = discount * np.array(
sum_chunks(tr[2, :] * z_states[map(int, tr[1, :])], ch))
z_actions[:, i] = out + reward_f[:, i]
m = np.amax(z_actions, axis=0)
z_states = m + np.log(np.sum(np.exp(z_actions - m), axis=0))
count += 1
#Action Probability Computation - - - - - - - - - - - - - - - -
delta = np.amax(np.absolute(prev - z_states))
if count == 50:
#print "Count and delta", count,delta
z_actions = z_actions
m = np.amax(z_actions, axis=0)
z_states = m + np.log(np.sum(np.exp(z_actions - m), axis=0))
policy = np.exp(z_actions - z_states)
break
return policy, np.log(policy), z_states
def caus_ent_backward_nodisount(transition, reward_f, steps):
num_actions = transition.tot_actions
num_states = transition.tot_states
if reward_f.shape[0] == num_actions:
state_action = True
else:
state_action = False
gamma = discount
z_actions = np.zeros([num_actions, num_states])
z_states = np.zeros(num_states)
#Backward - - - - - - - - - - - - - - - - - - - - - - - - - -
print "Caus Ent Backward"
count = 0
delta = 0
for j in range(steps):
prev = np.zeros(z_states.shape)
prev += z_states
for i in range(num_states):
tr = transition.dense_backward[i]
ch = transition.chunks_backward[i]
out = gamma * np.array(
sum_chunks(tr[2, :] * z_states[map(int, tr[1, :])], ch))
z_actions[:, i] = out + reward_f[:, i]
m = np.amax(z_actions)
z_states = m + np.log(np.sum(np.exp(z_actions - m), axis=0))
count += 1
#Action Probability Computation - - - - - - - - - - - - - - - -
delta = np.sum(np.sum(np.absolute(prev - z_states)))
#delta +=1
#print "DElta cause",delta,delta2
if j == steps - 1:
z_actions = z_actions
m = np.amax(z_actions)
z_states = m + np.log(np.sum(np.exp(z_actions - m), axis=0))
policy = np.exp(z_actions - z_states)
return policy, np.log(policy), z_states
