def iterate(self, XA, X1, u, A_low, A_high, ITER=50, Ascaled=False, plot=True, xargs=[], output=True, a = 0, b = 0, pc_samp=1, maxT=60000, eta=0.8, tilesg=False, sg_prop=0.96, sg_samp=1, sg_points=100, sgmem_max=0.4, plotiter=False, test=False, NS=False):
tic = time()
self.v_e = 0 # Value function error
self.p_e = 0 # Policy function error
T = XA.shape[0]
self.value_error = np.zeros(ITER)
if not(tilesg):
grid, m = buildgrid(X1, sg_points, self.radius, scale=True, stopnum=X1.shape[0])
else:
nn = int(X1.shape[0]*sg_samp)
tic = time()
tile = TilecodeSamplegrid(X1.shape[1], 25, mem_max=sgmem_max, cores=self.CORES)
grid = tile.fit(X1[0:nn], self.radius, prop=sg_prop)
toc = time()
print 'State grid points: ' + str(grid.shape[0]) + ', of maximum: ' + str(tile.max_points) + ', Time taken: ' + str(toc - tic)
del tile
points = grid.shape[0]
ticfit = time()
if self.first:
self.W_f.fit(grid, np.zeros(points), NS=NS)
self.V_f.fit(grid, np.zeros(points), NS=NS)
self.first = False
Al = np.zeros(points)
Ah = np.zeros(points)
if Ascaled:
for i in range(points):
Ws = self.W_f.predict(grid[i,:])
Al[i] = A_low(grid[i,:], Ws)
Ah[i] = A_high(grid[i,:], Ws)
minpol = 0
maxpol = 1
else:
for i in range(points):
Al[i] = A_low(grid[i,:])
Ah[i] = A_high(grid[i,:])
minpol = np.min(Al)
maxpol = np.max(Ah)
tocfit = time()
print 'Constraint time: ' + str(tocfit - ticfit)
if ITER == 1:
precompute = False
else:
precompute = True
# ------------------
# Q-learning
# ------------------
#First iteration
j = 0
# Q values
ticfit = time()
Q = u + self.beta * self.V_f.predict(X1, store_XS=precompute)
tocfit = time()
print 'V prediction time: ' + str(tocfit - ticfit)
# Fit Q function
ticfit = time()
self.Q_f.fit(XA, Q, pa=minpol, pb=maxpol , copy=True, unsupervised=precompute, sgd=self.asgd, asgd=self.asgd, eta=eta, n_iters=1, scale=1* (1 / min(T, maxT)), storeindex=(self.asgd and precompute), a=a, b=b, pc_samp=pc_samp)
tocfit = time()
print 'Q Fitting time: ' + str(tocfit - ticfit)
# Optimise Q function
self.value_error[0], W_opt, state = self.maximise(grid, Al, Ah, Ascaled, output=output, plotiter=plotiter, xargs=xargs, NS=NS)
for j in range(1, ITER):
# Q values
Q = u + self.beta * self.V_f.fast_values()
# Fit Q function
self.Q_f.partial_fit(Q, 0)
# Optimise Q function
self.value_error[j], W_opt, state = self.maximise(grid, Al, Ah, Ascaled, output=output, plotiter=plotiter, xargs=xargs, NS=NS)
ticfit = time()
NN = min(X1.shape[0], 20000)
W_opt_old = self.W_f.predict(X1[0:NN,:])
self.W_f.fit(state, W_opt, sgd=0, eta=0.1, n_iters=5, scale=0, NS=NS)
W_opt_new = self.W_f.predict(X1[0:NN,:])
self.pe = np.mean((W_opt_old - W_opt_new))/np.mean(W_opt_old)
toc = time()
tocfit = time()
print 'Policy time: ' + str(tocfit - ticfit)
print 'Solve time: ' + str(toc - tic) + ', Policy change: ' + str(self.pe)
if plot:
xargstemp = xargs
self.W_f.plot(xargs, showdata=True)
pylab.show()
self.V_f.plot(xargstemp, showdata=True)
pylab.show()
def iterate(self, XA, X1, u, A_low, A_high, ITER=50, Ascaled=False, plot=True, xargs =[], output=True, gridsamp=1):
tic = time()
self.v_e = 0 # Value function error
self.p_e = 0 # Policy function error
tic = time()
N = int(gridsamp * X1.shape[0])
grid, m = buildgrid(X1[0:N, :], self.maxgrid, self.radius, scale=True)
points = grid.shape[0]
toc = time()
print 'State grid points: ' + str(points) + ', of maximum: ' + str(m) + ', Time taken: ' + str(toc - tic)
if self.first:
self.W_f.fit(grid, np.zeros(points))
self.V_f.fit(grid, np.zeros(points))
self.first = False
Al = np.zeros(points)
Ah = np.zeros(points)
if Ascaled:
for i in range(points):
Ws = self.W_f_old.predict(grid[i,:])
Al[i] = A_low(grid[i,:], Ws)
Ah[i] = A_high(grid[i,:], Ws)
else:
for i in range(points):
Al[i] = A_low(grid[i,:])
Ah[i] = A_high(grid[i,:])
# ------------------
# Q-learning
# ------------------
#First iteration
j = 0
# Q values
Q = u + self.beta * self.V_f.predict(X1, store_XS=True)
# Fit Q function
self.Q_f.fit(XA, Q)
# Optimise Q function
ERROR = self.maximise(grid, Al, Ah, Ascaled, output=output)
for j in range(ITER):
# Q values
Q = u + self.beta * self.V_f.fast_values()
# Fit Q function
tic = time()
self.Q_f.fit(XA, Q)
toc = time()
print 'Fit time: ' + str(toc - tic)
# Optimise Q function
ERROR = self.maximise(grid, Al, Ah, Ascaled, output=output)
toc = time()
print 'Solve time: ' + str(toc - tic)
if plot:
self.W_f.plot(xargs, showdata=True)
pylab.show()
def iterate(self, XA, X1, u, A_low, A_high, ITER=50, plot=True, xargs=[], output=True, a = 0, b = 0, pc_samp=1, maxT=60000, eta=0.8, tilesg=False, sg_prop=0.96, sg_samp=1, sg_points=100, sgmem_max=0.4, plotiter=False, test=False, NS=False):
tic = time()
M = self.M
self.v_e = 0 # Value function error
self.p_e = 0 # Policy function error
T = [XA[m].shape[0] for m in M]
self.value_error = np.zeros(ITER)
grid = [0,0]
tile = [0,0]
if not(tilesg):
grid[0], _ = buildgrid(X1[0], sg_points, self.radius, scale=True, stopnum=X1[0].shape[0])
grid[1], _ = buildgrid(X1[1], sg_points, self.radius, scale=True, stopnum=X1[1].shape[0])
else:
for m in range(2):
nn = int(X1[m].shape[0]*sg_samp)
tic = time()
tile[m] = TilecodeSamplegrid(X1[m].shape[1], 25, mem_max=sgmem_max, cores=self.CORES)
grid[m] = tile[m].fit(X1[m][0:nn], self.radius, prop=sg_prop)
toc = time()
print 'State grid points: ' + str(grid[m].shape[0]) + ', of maximum: ' + str(tile[m].max_points) + ', Time taken: ' + str(toc - tic)
del tile
#import pdb; pdb.set_trace()
points = [grid[m].shape[0] for m in M]
ticfit = time()
if self.first:
[self.W_f[m].fit(grid[m], np.zeros(points[m]), NS=NS) for m in M]
[self.V_f[m].fit(grid[m], np.zeros(points[m]), NS=NS) for m in M]
self.first = False
Al = [np.zeros(points[m]) for m in M]
Ah = [np.zeros(points[m]) for m in M]
for m in range(2):
for i in range(points[m]):
Al[m][i] = A_low(grid[m][i,:])
Ah[m][i] = A_high(grid[m][i,:])
minpol = [np.min(Al[m]) for m in M]
maxpol = [np.max(Ah[m]) for m in M]
tocfit = time()
print 'Constraint time: ' + str(tocfit - ticfit)
if ITER == 1:
precompute = False
else:
precompute = True
W_opt = [0,0]
state = [0,0]
Q = [0,0]
# ------------------
# Q-learning
# ------------------
#First iteration
j = 0
ticfit = time()
m1 = 1
for m in range(2):
# Q values
Q[m] = u[m] + self.beta * self.V_f[m1].predict(X1[m], store_XS=precompute)
tocfit = time()
print 'V prediction time: ' + str(tocfit - ticfit)
# Fit Q function
ticfit = time()
self.Q_f[m].fit(XA[m], Q[m], pa=minpol[m], pb=maxpol[m] , copy=True, unsupervised=precompute, sgd=self.asgd, asgd=self.asgd, eta=eta, n_iters=1, scale=1* (1 / min(T[m], maxT)), storeindex=(self.asgd and precompute), a=a, b=b, pc_samp=pc_samp)
tocfit = time()
print 'Q Fitting time: ' + str(tocfit - ticfit)
# Optimise Q function
value_error, W_opt[m], state[m] = self.maximise(m, grid[m], Al[m], Ah[m], output=output, plotiter=plotiter, xargs=xargs, NS=NS)
m1 = 0
if test:
import pdb; pdb.set_trace()
for j in range(1, ITER):
m1 = 1
for m in range(2):
# Q values
Q[m] = u[m] + self.beta * self.V_f[m1].fast_values()
# Fit Q function
self.Q_f[m].partial_fit(Q[m], 0)
# Optimise Q function
value_error, W_opt[m], state[m] = self.maximise(m, grid[m], Al[m], Ah[m], output=output, plotiter=plotiter, xargs=xargs, NS=NS)
m1 = 0
if test:
import pdb; pdb.set_trace()
self.pe = [0,0]
for m in range(2):
ticfit = time()
NN = min(X1[m].shape[0], 20000)
W_opt_old = self.W_f[m].predict(X1[m][0:NN,:])
self.W_f[m].fit(state[m], W_opt[m], sgd=0, eta=0.1, n_iters=5, scale=0, NS=NS)
W_opt_new = self.W_f[m].predict(X1[m][0:NN,:])
self.pe[m] = np.mean((W_opt_old - W_opt_new)/W_opt_old)
toc = time()
tocfit = time()
print 'Policy time: ' + str(tocfit - ticfit)
print 'Solve time: ' + str(toc - tic) + ', Policy change: ' + str(self.pe)
if plot:
xargstemp1 = xargs
xargstemp2 = xargs
for m in range(2):
xargs1 = xargstemp1
self.W_f[m].plot(xargs1, showdata=True)
pylab.show()
xargs2 = xargstemp2
self.V_f[m].plot(xargs2, showdata=True)
pylab.show()
xargstemp1 = xargs
xargstemp2 = xargs
def iterate(self,
XA,
X1,
u,
A_low,
A_high,
ITER=50,
Ascaled=False,
plot=True,
xargs=[],
output=True,
a=0,
b=0,
pc_samp=1,
maxT=60000,
eta=0.8,
tilesg=False,
sg_prop=0.96,
sg_samp=1,
sg_points=100,
sgmem_max=0.4,
plotiter=False,
test=False,
NS=False):
tic = time()
self.v_e = 0 # Value function error
self.p_e = 0 # Policy function error
T = XA.shape[0]
self.value_error = np.zeros(ITER)
if not (tilesg):
grid, m = buildgrid(X1,
sg_points,
self.radius,
scale=True,
stopnum=X1.shape[0])
else:
nn = int(X1.shape[0] * sg_samp)
tic = time()
tile = TilecodeSamplegrid(X1.shape[1],
25,
mem_max=sgmem_max,
cores=self.CORES)
grid = tile.fit(X1[0:nn], self.radius, prop=sg_prop)
toc = time()
print 'State grid points: ' + str(
grid.shape[0]) + ', of maximum: ' + str(
tile.max_points) + ', Time taken: ' + str(toc - tic)
del tile
points = grid.shape[0]
ticfit = time()
if self.first:
self.W_f.fit(grid, np.zeros(points), NS=NS)
self.V_f.fit(grid, np.zeros(points), NS=NS)
self.first = False
Al = np.zeros(points)
Ah = np.zeros(points)
if Ascaled:
for i in range(points):
Ws = self.W_f.predict(grid[i, :])
Al[i] = A_low(grid[i, :], Ws)
Ah[i] = A_high(grid[i, :], Ws)
minpol = 0
maxpol = 1
else:
for i in range(points):
Al[i] = A_low(grid[i, :])
Ah[i] = A_high(grid[i, :])
minpol = np.min(Al)
maxpol = np.max(Ah)
tocfit = time()
print 'Constraint time: ' + str(tocfit - ticfit)
if ITER == 1:
precompute = False
else:
precompute = True
# ------------------
# Q-learning
# ------------------
#First iteration
j = 0
# Q values
ticfit = time()
Q = u + self.beta * self.V_f.predict(X1, store_XS=precompute)
tocfit = time()
print 'V prediction time: ' + str(tocfit - ticfit)
# Fit Q function
ticfit = time()
self.Q_f.fit(XA,
Q,
pa=minpol,
pb=maxpol,
copy=True,
unsupervised=precompute,
sgd=self.asgd,
asgd=self.asgd,
eta=eta,
n_iters=1,
scale=1 * (1 / min(T, maxT)),
storeindex=(self.asgd and precompute),
a=a,
b=b,
pc_samp=pc_samp)
tocfit = time()
print 'Q Fitting time: ' + str(tocfit - ticfit)
# Optimise Q function
self.value_error[0], W_opt, state = self.maximise(grid,
Al,
Ah,
Ascaled,
output=output,
plotiter=plotiter,
xargs=xargs,
NS=NS)
for j in range(1, ITER):
# Q values
Q = u + self.beta * self.V_f.fast_values()
# Fit Q function
self.Q_f.partial_fit(Q, 0)
# Optimise Q function
self.value_error[j], W_opt, state = self.maximise(
grid,
Al,
Ah,
Ascaled,
output=output,
plotiter=plotiter,
xargs=xargs,
NS=NS)
ticfit = time()
NN = min(X1.shape[0], 20000)
W_opt_old = self.W_f.predict(X1[0:NN, :])
self.W_f.fit(state, W_opt, sgd=0, eta=0.1, n_iters=5, scale=0, NS=NS)
W_opt_new = self.W_f.predict(X1[0:NN, :])
self.pe = np.mean((W_opt_old - W_opt_new)) / np.mean(W_opt_old)
toc = time()
tocfit = time()
print 'Policy time: ' + str(tocfit - ticfit)
print 'Solve time: ' + str(toc - tic) + ', Policy change: ' + str(
self.pe)
if plot:
xargstemp = xargs
self.W_f.plot(xargs, showdata=True)
pylab.show()
self.V_f.plot(xargstemp, showdata=True)
pylab.show()
def iterate(self,
XA,
X1,
u,
A_low,
A_high,
ITER=50,
Ascaled=False,
plot=True,
xargs=[],
output=True,
gridsamp=1):
tic = time()
self.v_e = 0 # Value function error
self.p_e = 0 # Policy function error
tic = time()
N = int(gridsamp * X1.shape[0])
grid, m = buildgrid(X1[0:N, :], self.maxgrid, self.radius, scale=True)
points = grid.shape[0]
toc = time()
print 'State grid points: ' + str(points) + ', of maximum: ' + str(
m) + ', Time taken: ' + str(toc - tic)
if self.first:
self.W_f.fit(grid, np.zeros(points))
self.V_f.fit(grid, np.zeros(points))
self.first = False
Al = np.zeros(points)
Ah = np.zeros(points)
if Ascaled:
for i in range(points):
Ws = self.W_f_old.predict(grid[i, :])
Al[i] = A_low(grid[i, :], Ws)
Ah[i] = A_high(grid[i, :], Ws)
else:
for i in range(points):
Al[i] = A_low(grid[i, :])
Ah[i] = A_high(grid[i, :])
# ------------------
# Q-learning
# ------------------
#First iteration
j = 0
# Q values
Q = u + self.beta * self.V_f.predict(X1, store_XS=True)
# Fit Q function
self.Q_f.fit(XA, Q)
# Optimise Q function
ERROR = self.maximise(grid, Al, Ah, Ascaled, output=output)
for j in range(ITER):
# Q values
Q = u + self.beta * self.V_f.fast_values()
# Fit Q function
tic = time()
self.Q_f.fit(XA, Q)
toc = time()
print 'Fit time: ' + str(toc - tic)
# Optimise Q function
ERROR = self.maximise(grid, Al, Ah, Ascaled, output=output)
toc = time()
print 'Solve time: ' + str(toc - tic)
if plot:
self.W_f.plot(xargs, showdata=True)
pylab.show()
def iterate(self,
XA,
X1,
u,
A_low,
A_high,
ITER=50,
plot=True,
xargs=[],
output=True,
a=0,
b=0,
pc_samp=1,
maxT=60000,
eta=0.8,
tilesg=False,
sg_prop=0.96,
sg_samp=1,
sg_points=100,
sgmem_max=0.4,
plotiter=False,
test=False,
NS=False):
tic = time()
M = self.M
self.v_e = 0 # Value function error
self.p_e = 0 # Policy function error
T = [XA[m].shape[0] for m in M]
self.value_error = np.zeros(ITER)
grid = [0, 0]
tile = [0, 0]
if not (tilesg):
grid[0], _ = buildgrid(X1[0],
sg_points,
self.radius,
scale=True,
stopnum=X1[0].shape[0])
grid[1], _ = buildgrid(X1[1],
sg_points,
self.radius,
scale=True,
stopnum=X1[1].shape[0])
else:
for m in range(2):
nn = int(X1[m].shape[0] * sg_samp)
tic = time()
tile[m] = TilecodeSamplegrid(X1[m].shape[1],
25,
mem_max=sgmem_max,
cores=self.CORES)
grid[m] = tile[m].fit(X1[m][0:nn], self.radius, prop=sg_prop)
toc = time()
print 'State grid points: ' + str(
grid[m].shape[0]) + ', of maximum: ' + str(
tile[m].max_points) + ', Time taken: ' + str(toc - tic)
del tile
#import pdb; pdb.set_trace()
points = [grid[m].shape[0] for m in M]
ticfit = time()
if self.first:
[self.W_f[m].fit(grid[m], np.zeros(points[m]), NS=NS) for m in M]
[self.V_f[m].fit(grid[m], np.zeros(points[m]), NS=NS) for m in M]
self.first = False
Al = [np.zeros(points[m]) for m in M]
Ah = [np.zeros(points[m]) for m in M]
for m in range(2):
for i in range(points[m]):
Al[m][i] = A_low(grid[m][i, :])
Ah[m][i] = A_high(grid[m][i, :])
minpol = [np.min(Al[m]) for m in M]
maxpol = [np.max(Ah[m]) for m in M]
tocfit = time()
print 'Constraint time: ' + str(tocfit - ticfit)
if ITER == 1:
precompute = False
else:
precompute = True
W_opt = [0, 0]
state = [0, 0]
Q = [0, 0]
# ------------------
# Q-learning
# ------------------
#First iteration
j = 0
ticfit = time()
m1 = 1
for m in range(2):
# Q values
Q[m] = u[m] + self.beta * self.V_f[m1].predict(X1[m],
store_XS=precompute)
tocfit = time()
print 'V prediction time: ' + str(tocfit - ticfit)
# Fit Q function
ticfit = time()
self.Q_f[m].fit(XA[m],
Q[m],
pa=minpol[m],
pb=maxpol[m],
copy=True,
unsupervised=precompute,
sgd=self.asgd,
asgd=self.asgd,
eta=eta,
n_iters=1,
scale=1 * (1 / min(T[m], maxT)),
storeindex=(self.asgd and precompute),
a=a,
b=b,
pc_samp=pc_samp)
tocfit = time()
print 'Q Fitting time: ' + str(tocfit - ticfit)
# Optimise Q function
value_error, W_opt[m], state[m] = self.maximise(m,
grid[m],
Al[m],
Ah[m],
output=output,
plotiter=plotiter,
xargs=xargs,
NS=NS)
m1 = 0
if test:
import pdb
pdb.set_trace()
for j in range(1, ITER):
m1 = 1
for m in range(2):
# Q values
Q[m] = u[m] + self.beta * self.V_f[m1].fast_values()
# Fit Q function
self.Q_f[m].partial_fit(Q[m], 0)
# Optimise Q function
value_error, W_opt[m], state[m] = self.maximise(
m,
grid[m],
Al[m],
Ah[m],
output=output,
plotiter=plotiter,
xargs=xargs,
NS=NS)
m1 = 0
if test:
import pdb
pdb.set_trace()
self.pe = [0, 0]
for m in range(2):
ticfit = time()
NN = min(X1[m].shape[0], 20000)
W_opt_old = self.W_f[m].predict(X1[m][0:NN, :])
self.W_f[m].fit(state[m],
W_opt[m],
sgd=0,
eta=0.1,
n_iters=5,
scale=0,
NS=NS)
W_opt_new = self.W_f[m].predict(X1[m][0:NN, :])
self.pe[m] = np.mean((W_opt_old - W_opt_new) / W_opt_old)
toc = time()
tocfit = time()
print 'Policy time: ' + str(tocfit - ticfit)
print 'Solve time: ' + str(toc - tic) + ', Policy change: ' + str(
self.pe)
if plot:
xargstemp1 = xargs
xargstemp2 = xargs
for m in range(2):
xargs1 = xargstemp1
self.W_f[m].plot(xargs1, showdata=True)
pylab.show()
xargs2 = xargstemp2
self.V_f[m].plot(xargs2, showdata=True)
pylab.show()
xargstemp1 = xargs
xargstemp2 = xargs
