def showDomain(self, a=0, s=None):
if s is None:
s = self.state
# Draw the environment
if self.domain_fig is None:
self.agent_fig = plt.figure("Domain")
self.domain_fig = plt.imshow(self.map,
cmap='GridWorld',
interpolation='nearest',
vmin=0,
vmax=5)
plt.xticks(np.arange(self.COLS), fontsize=FONTSIZE)
plt.yticks(np.arange(self.ROWS), fontsize=FONTSIZE)
# pl.tight_layout()
self.agent_fig = plt.gca().plot(s[1],
s[0],
'kd',
markersize=20.0 - self.COLS)
plt.show()
self.agent_fig.pop(0).remove()
self.agent_fig = plt.figure("Domain")
#mapcopy = copy(self.map)
#mapcopy[s[0],s[1]] = self.AGENT
# self.domain_fig.set_data(mapcopy)
# Instead of '>' you can use 'D', 'o'
self.agent_fig = plt.gca().plot(s[1],
s[0],
'k>',
markersize=20.0 - self.COLS)
plt.draw()
def showDomain(self, a=0):
# Draw the environment
s = self.state
world = np.zeros((self.blocks, self.blocks), 'uint8')
undrawn_blocks = np.arange(self.blocks)
while len(undrawn_blocks):
A = undrawn_blocks[0]
B = s[A]
undrawn_blocks = undrawn_blocks[1:]
if B == A: # => A is on Table
world[0, A] = A + 1 # 0 is white thats why!
else:
# See if B is already drawn
i, j = findElemArray2D(B + 1, world)
if len(i):
world[i + 1, j] = A + 1 # 0 is white thats why!
else:
# Put it in the back of the list
undrawn_blocks = np.hstack((undrawn_blocks, [A]))
if self.domain_fig is None:
self.domain_fig = plt.imshow(
world,
cmap='BlocksWorld',
origin='lower',
interpolation='nearest') # ,vmin=0,vmax=self.blocks)
plt.xticks(np.arange(self.blocks), fontsize=FONTSIZE)
plt.yticks(np.arange(self.blocks), fontsize=FONTSIZE)
# pl.tight_layout()
plt.axis('off')
plt.show()
else:
self.domain_fig.set_data(world)
plt.draw()
def showDomain(self, a=0):
s = self.state
# Draw the environment
if self.domain_fig is None:
self.move_fig = plt.subplot(111)
s = s.reshape((self.BOARD_SIZE, self.BOARD_SIZE))
self.domain_fig = plt.imshow(s,
cmap='FlipBoard',
interpolation='nearest',
vmin=0,
vmax=1)
plt.xticks(np.arange(self.BOARD_SIZE), fontsize=FONTSIZE)
plt.yticks(np.arange(self.BOARD_SIZE), fontsize=FONTSIZE)
# pl.tight_layout()
a_row, a_col = id2vec(a, [self.BOARD_SIZE, self.BOARD_SIZE])
self.move_fig = self.move_fig.plot(a_col,
a_row,
'kx',
markersize=30.0)
plt.show()
a_row, a_col = id2vec(a, [self.BOARD_SIZE, self.BOARD_SIZE])
self.move_fig.pop(0).remove()
# print a_row,a_col
# Instead of '>' you can use 'D', 'o'
self.move_fig = plt.plot(a_col, a_row, 'kx', markersize=30.0)
s = s.reshape((self.BOARD_SIZE, self.BOARD_SIZE))
self.domain_fig.set_data(s)
plt.draw()
def showDomain(self, a=0, s=None):
if s is None:
s = self.state
# Draw the environment
if self.domain_fig is None:
self.agent_fig = plt.figure("Domain")
self.domain_fig = plt.imshow(
self.map,
cmap='GridWorld',
interpolation='nearest',
vmin=0,
vmax=5)
plt.xticks(np.arange(self.COLS), fontsize=FONTSIZE)
plt.yticks(np.arange(self.ROWS), fontsize=FONTSIZE)
# pl.tight_layout()
self.agent_fig = plt.gca(
).plot(s[1],
s[0],
'kd',
markersize=20.0 - self.COLS)
plt.show()
self.agent_fig.pop(0).remove()
self.agent_fig = plt.figure("Domain")
#mapcopy = copy(self.map)
#mapcopy[s[0],s[1]] = self.AGENT
# self.domain_fig.set_data(mapcopy)
# Instead of '>' you can use 'D', 'o'
self.agent_fig = plt.gca(
).plot(s[1],
s[0],
'k>',
markersize=20.0 - self.COLS)
plt.draw()
def showDomain(self, a=0):
s = self.state
# Draw the environment
if self.domain_fig is None:
self.move_fig = plt.subplot(111)
s = s.reshape((self.BOARD_SIZE, self.BOARD_SIZE))
self.domain_fig = plt.imshow(
s,
cmap='FlipBoard',
interpolation='nearest',
vmin=0,
vmax=1)
plt.xticks(np.arange(self.BOARD_SIZE), fontsize=FONTSIZE)
plt.yticks(np.arange(self.BOARD_SIZE), fontsize=FONTSIZE)
# pl.tight_layout()
a_row, a_col = id2vec(a, [self.BOARD_SIZE, self.BOARD_SIZE])
self.move_fig = self.move_fig.plot(
a_col,
a_row,
'kx',
markersize=30.0)
plt.show()
a_row, a_col = id2vec(a, [self.BOARD_SIZE, self.BOARD_SIZE])
self.move_fig.pop(0).remove()
# print a_row,a_col
# Instead of '>' you can use 'D', 'o'
self.move_fig = plt.plot(a_col, a_row, 'kx', markersize=30.0)
s = s.reshape((self.BOARD_SIZE, self.BOARD_SIZE))
self.domain_fig.set_data(s)
plt.draw()
def showDomain(self, a):
s = self.state
# Draw the environment
if self.domain_fig is None:
self.domain_fig = plt.imshow(self.map,
cmap='IntruderMonitoring',
interpolation='nearest',
vmin=0,
vmax=3)
plt.xticks(np.arange(self.COLS), fontsize=FONTSIZE)
plt.yticks(np.arange(self.ROWS), fontsize=FONTSIZE)
plt.show()
if self.ally_fig is not None:
self.ally_fig.pop(0).remove()
self.intruder_fig.pop(0).remove()
s_ally = s[0:self.NUMBER_OF_AGENTS * 2].reshape((-1, 2))
s_intruder = s[self.NUMBER_OF_AGENTS * 2:].reshape((-1, 2))
self.ally_fig = plt.plot(s_ally[:, 1],
s_ally[:, 0],
'bo',
markersize=30.0,
alpha=.7,
markeredgecolor='k',
markeredgewidth=2)
self.intruder_fig = plt.plot(s_intruder[:, 1],
s_intruder[:, 0],
'g>',
color='gray',
markersize=30.0,
alpha=.7,
markeredgecolor='k',
markeredgewidth=2)
plt.draw()
def showDomain(self, a=0):
# Draw the environment
s = self.state
world = np.zeros((self.blocks, self.blocks), 'uint8')
undrawn_blocks = np.arange(self.blocks)
while len(undrawn_blocks):
A = undrawn_blocks[0]
B = s[A]
undrawn_blocks = undrawn_blocks[1:]
if B == A: # => A is on Table
world[0, A] = A + 1 # 0 is white thats why!
else:
# See if B is already drawn
i, j = findElemArray2D(B + 1, world)
if len(i):
world[i + 1, j] = A + 1 # 0 is white thats why!
else:
# Put it in the back of the list
undrawn_blocks = np.hstack((undrawn_blocks, [A]))
if self.domain_fig is None:
plt.figure("Domain")
self.domain_fig = plt.imshow(
world,
cmap='BlocksWorld',
origin='lower',
interpolation='nearest') # ,vmin=0,vmax=self.blocks)
plt.xticks(np.arange(self.blocks), fontsize=FONTSIZE)
plt.yticks(np.arange(self.blocks), fontsize=FONTSIZE)
# pl.tight_layout()
plt.axis('off')
plt.show()
else:
self.domain_fig.set_data(world)
plt.figure("Domain").canvas.draw()
plt.figure("Domain").canvas.flush_events()
def showLearning(self, representation):
pi = np.zeros(
(self.X_discretization,
self.XDot_discretization),
'uint8')
V = np.zeros((self.X_discretization, self.XDot_discretization))
if self.valueFunction_fig is None:
self.valueFunction_fig = plt.figure("Value Function")
self.valueFunction_im = plt.imshow(
V,
cmap='ValueFunction',
interpolation='nearest',
origin='lower',
vmin=self.MIN_RETURN,
vmax=self.MAX_RETURN)
plt.xticks(self.xTicks, self.xTicksLabels, fontsize=12)
plt.yticks(self.yTicks, self.yTicksLabels, fontsize=12)
plt.xlabel(r"$x$")
plt.ylabel(r"$\dot x$")
self.policy_fig = plt.figure("Policy")
self.policy_im = plt.imshow(
pi,
cmap='MountainCarActions',
interpolation='nearest',
origin='lower',
vmin=0,
vmax=self.actions_num)
plt.xticks(self.xTicks, self.xTicksLabels, fontsize=12)
plt.yticks(self.yTicks, self.yTicksLabels, fontsize=12)
plt.xlabel(r"$x$")
plt.ylabel(r"$\dot x$")
plt.show()
for row, xDot in enumerate(np.linspace(self.XDOTMIN, self.XDOTMAX, self.XDot_discretization)):
for col, x in enumerate(np.linspace(self.XMIN, self.XMAX, self.X_discretization)):
s = [x, xDot]
Qs = representation.Qs(s, False)
As = self.possibleActions()
pi[row, col] = representation.bestAction(s, False, As)
V[row, col] = max(Qs)
self.valueFunction_im.set_data(V)
self.policy_im.set_data(pi)
self.valueFunction_fig = plt.figure("Value Function")
plt.draw()
self.policy_fig = plt.figure("Policy")
plt.draw()
def showDomain(self, a):
s = self.state
# Draw the environment
if self.domain_fig is None:
plt.figure("Domain")
self.domain_fig = plt.imshow(
self.map,
cmap='IntruderMonitoring',
interpolation='nearest',
vmin=0,
vmax=3)
plt.xticks(np.arange(self.COLS), fontsize=FONTSIZE)
plt.yticks(np.arange(self.ROWS), fontsize=FONTSIZE)
plt.show()
if self.ally_fig is not None:
self.ally_fig.pop(0).remove()
self.intruder_fig.pop(0).remove()
s_ally = s[0:self.NUMBER_OF_AGENTS * 2].reshape((-1, 2))
s_intruder = s[self.NUMBER_OF_AGENTS * 2:].reshape((-1, 2))
self.ally_fig = plt.plot(
s_ally[:,
1],
s_ally[:,
0],
'bo',
markersize=30.0,
alpha=.7,
markeredgecolor='k',
markeredgewidth=2)
self.intruder_fig = plt.plot(
s_intruder[:,
1],
s_intruder[:,
0],
'g>',
color='gray',
markersize=30.0,
alpha=.7,
markeredgecolor='k',
markeredgewidth=2)
plt.figure("Domain").canvas.draw()
plt.figure("Domain").canvas.flush_events()
def gridworld_showlearning(self, representation):
dom = self.actual_domain
if self.valueFunction_fig is None:
plt.figure("Value Function")
self.valueFunction_fig = plt.imshow(dom.map,
cmap='ValueFunction',
interpolation='nearest',
vmin=dom.MIN_RETURN,
vmax=dom.MAX_RETURN)
plt.xticks(np.arange(dom.COLS), fontsize=12)
plt.yticks(np.arange(dom.ROWS), fontsize=12)
# Create quivers for each action. 4 in total
plt.show()
plt.figure("Value Function")
V = self.get_value_function(representation)
# print Acts
# Show Value Function
self.valueFunction_fig.set_data(V)
plt.draw()
def showLearning(self, representation):
if self.valueFunction_fig is None:
plt.figure("Value Function")
self.valueFunction_fig = plt.imshow(self.map,
cmap='ValueFunction',
interpolation='nearest',
vmin=self.MIN_RETURN,
vmax=self.MAX_RETURN)
plt.xticks(np.arange(self.COLS), fontsize=12)
plt.yticks(np.arange(self.ROWS), fontsize=12)
# Create quivers for each action. 4 in total
X = np.arange(self.ROWS) - self.SHIFT
Y = np.arange(self.COLS)
X, Y = np.meshgrid(X, Y)
DX = DY = np.ones(X.shape)
C = np.zeros(X.shape)
C[0, 0] = 1 # Making sure C has both 0 and 1
# length of arrow/width of bax. Less then 0.5 because each arrow is
# offset, 0.4 looks nice but could be better/auto generated
arrow_ratio = 0.4
Max_Ratio_ArrowHead_to_ArrowLength = 0.25
ARROW_WIDTH = 0.5 * Max_Ratio_ArrowHead_to_ArrowLength / 5.0
self.upArrows_fig = plt.quiver(Y,
X,
DY,
DX,
C,
units='y',
cmap='Actions',
scale_units="height",
scale=self.ROWS / arrow_ratio,
width=-1 * ARROW_WIDTH)
self.upArrows_fig.set_clim(vmin=0, vmax=1)
X = np.arange(self.ROWS) + self.SHIFT
Y = np.arange(self.COLS)
X, Y = np.meshgrid(X, Y)
self.downArrows_fig = plt.quiver(Y,
X,
DY,
DX,
C,
units='y',
cmap='Actions',
scale_units="height",
scale=self.ROWS / arrow_ratio,
width=-1 * ARROW_WIDTH)
self.downArrows_fig.set_clim(vmin=0, vmax=1)
X = np.arange(self.ROWS)
Y = np.arange(self.COLS) - self.SHIFT
X, Y = np.meshgrid(X, Y)
self.leftArrows_fig = plt.quiver(Y,
X,
DY,
DX,
C,
units='x',
cmap='Actions',
scale_units="width",
scale=self.COLS / arrow_ratio,
width=ARROW_WIDTH)
self.leftArrows_fig.set_clim(vmin=0, vmax=1)
X = np.arange(self.ROWS)
Y = np.arange(self.COLS) + self.SHIFT
X, Y = np.meshgrid(X, Y)
self.rightArrows_fig = plt.quiver(Y,
X,
DY,
DX,
C,
units='x',
cmap='Actions',
scale_units="width",
scale=self.COLS / arrow_ratio,
width=ARROW_WIDTH)
self.rightArrows_fig.set_clim(vmin=0, vmax=1)
plt.show()
plt.figure("Value Function")
V = np.zeros((self.ROWS, self.COLS))
# Boolean 3 dimensional array. The third array highlights the action.
# Thie mask is used to see in which cells what actions should exist
Mask = np.ones((self.COLS, self.ROWS, self.actions_num), dtype='bool')
arrowSize = np.zeros((self.COLS, self.ROWS, self.actions_num),
dtype='float')
# 0 = suboptimal action, 1 = optimal action
arrowColors = np.zeros((self.COLS, self.ROWS, self.actions_num),
dtype='uint8')
for r in xrange(self.ROWS):
for c in xrange(self.COLS):
if self.map[r, c] == self.BLOCKED:
V[r, c] = 0
if self.map[r, c] == self.GOAL:
V[r, c] = self.MAX_RETURN
if self.map[r, c] == self.PIT:
V[r, c] = self.MIN_RETURN
if self.map[r, c] == self.EMPTY or self.map[r,
c] == self.START:
s = np.array([r, c])
As = self.possibleActions(s)
terminal = self.isTerminal(s)
Qs = representation.Qs(s, terminal)
bestA = representation.bestActions(s, terminal, As)
V[r, c] = max(Qs[As])
Mask[c, r, As] = False
arrowColors[c, r, bestA] = 1
for i in xrange(len(As)):
a = As[i]
Q = Qs[i]
value = linearMap(Q, self.MIN_RETURN, self.MAX_RETURN,
0, 1)
arrowSize[c, r, a] = value
# Show Value Function
self.valueFunction_fig.set_data(V)
# Show Policy Up Arrows
DX = arrowSize[:, :, 0]
DY = np.zeros((self.ROWS, self.COLS))
DX = np.ma.masked_array(DX, mask=Mask[:, :, 0])
DY = np.ma.masked_array(DY, mask=Mask[:, :, 0])
C = np.ma.masked_array(arrowColors[:, :, 0], mask=Mask[:, :, 0])
self.upArrows_fig.set_UVC(DY, DX, C)
# Show Policy Down Arrows
DX = -arrowSize[:, :, 1]
DY = np.zeros((self.ROWS, self.COLS))
DX = np.ma.masked_array(DX, mask=Mask[:, :, 1])
DY = np.ma.masked_array(DY, mask=Mask[:, :, 1])
C = np.ma.masked_array(arrowColors[:, :, 1], mask=Mask[:, :, 1])
self.downArrows_fig.set_UVC(DY, DX, C)
# Show Policy Left Arrows
DX = np.zeros((self.ROWS, self.COLS))
DY = -arrowSize[:, :, 2]
DX = np.ma.masked_array(DX, mask=Mask[:, :, 2])
DY = np.ma.masked_array(DY, mask=Mask[:, :, 2])
C = np.ma.masked_array(arrowColors[:, :, 2], mask=Mask[:, :, 2])
self.leftArrows_fig.set_UVC(DY, DX, C)
# Show Policy Right Arrows
DX = np.zeros((self.ROWS, self.COLS))
DY = arrowSize[:, :, 3]
DX = np.ma.masked_array(DX, mask=Mask[:, :, 3])
DY = np.ma.masked_array(DY, mask=Mask[:, :, 3])
C = np.ma.masked_array(arrowColors[:, :, 3], mask=Mask[:, :, 3])
self.rightArrows_fig.set_UVC(DY, DX, C)
plt.draw()
def showLearning(self, representation):
if self.valueFunction_fig is None:
plt.figure("Value Function")
self.valueFunction_fig = plt.imshow(
self.map,
cmap='ValueFunction',
interpolation='nearest',
vmin=self.MIN_RETURN,
vmax=self.MAX_RETURN)
plt.xticks(np.arange(self.COLS), fontsize=12)
plt.yticks(np.arange(self.ROWS), fontsize=12)
# Create quivers for each action. 4 in total
X = np.arange(self.ROWS) - self.SHIFT
Y = np.arange(self.COLS)
X, Y = np.meshgrid(X, Y)
DX = DY = np.ones(X.shape)
C = np.zeros(X.shape)
C[0, 0] = 1 # Making sure C has both 0 and 1
# length of arrow/width of bax. Less then 0.5 because each arrow is
# offset, 0.4 looks nice but could be better/auto generated
arrow_ratio = 0.4
Max_Ratio_ArrowHead_to_ArrowLength = 0.25
ARROW_WIDTH = 0.5 * Max_Ratio_ArrowHead_to_ArrowLength / 5.0
self.upArrows_fig = plt.quiver(
Y,
X,
DY,
DX,
C,
units='y',
cmap='Actions',
scale_units="height",
scale=self.ROWS /
arrow_ratio,
width=-
1 *
ARROW_WIDTH)
self.upArrows_fig.set_clim(vmin=0, vmax=1)
X = np.arange(self.ROWS) + self.SHIFT
Y = np.arange(self.COLS)
X, Y = np.meshgrid(X, Y)
self.downArrows_fig = plt.quiver(
Y,
X,
DY,
DX,
C,
units='y',
cmap='Actions',
scale_units="height",
scale=self.ROWS /
arrow_ratio,
width=-
1 *
ARROW_WIDTH)
self.downArrows_fig.set_clim(vmin=0, vmax=1)
X = np.arange(self.ROWS)
Y = np.arange(self.COLS) - self.SHIFT
X, Y = np.meshgrid(X, Y)
self.leftArrows_fig = plt.quiver(
Y,
X,
DY,
DX,
C,
units='x',
cmap='Actions',
scale_units="width",
scale=self.COLS /
arrow_ratio,
width=ARROW_WIDTH)
self.leftArrows_fig.set_clim(vmin=0, vmax=1)
X = np.arange(self.ROWS)
Y = np.arange(self.COLS) + self.SHIFT
X, Y = np.meshgrid(X, Y)
self.rightArrows_fig = plt.quiver(
Y,
X,
DY,
DX,
C,
units='x',
cmap='Actions',
scale_units="width",
scale=self.COLS /
arrow_ratio,
width=ARROW_WIDTH)
self.rightArrows_fig.set_clim(vmin=0, vmax=1)
plt.show()
plt.figure("Value Function")
V = np.zeros((self.ROWS, self.COLS))
# Boolean 3 dimensional array. The third array highlights the action.
# Thie mask is used to see in which cells what actions should exist
Mask = np.ones(
(self.COLS,
self.ROWS,
self.actions_num),
dtype='bool')
arrowSize = np.zeros(
(self.COLS,
self.ROWS,
self.actions_num),
dtype='float')
# 0 = suboptimal action, 1 = optimal action
arrowColors = np.zeros(
(self.COLS,
self.ROWS,
self.actions_num),
dtype='uint8')
for r in xrange(self.ROWS):
for c in xrange(self.COLS):
if self.map[r, c] == self.BLOCKED:
V[r, c] = 0
if self.map[r, c] == self.GOAL:
V[r, c] = self.MAX_RETURN
if self.map[r, c] == self.PIT:
V[r, c] = self.MIN_RETURN
if self.map[r, c] == self.EMPTY or self.map[r, c] == self.START:
s = np.array([r, c])
As = self.possibleActions(s)
terminal = self.isTerminal(s)
Qs = representation.Qs(s, terminal)
bestA = representation.bestActions(s, terminal, As)
V[r, c] = max(Qs[As])
Mask[c, r, As] = False
arrowColors[c, r, bestA] = 1
for i in xrange(len(As)):
a = As[i]
Q = Qs[i]
value = linearMap(
Q,
self.MIN_RETURN,
self.MAX_RETURN,
0,
1)
arrowSize[c, r, a] = value
# Show Value Function
self.valueFunction_fig.set_data(V)
# Show Policy Up Arrows
DX = arrowSize[:, :, 0]
DY = np.zeros((self.ROWS, self.COLS))
DX = np.ma.masked_array(DX, mask=Mask[:, :, 0])
DY = np.ma.masked_array(DY, mask=Mask[:, :, 0])
C = np.ma.masked_array(arrowColors[:, :, 0], mask=Mask[:,:, 0])
self.upArrows_fig.set_UVC(DY, DX, C)
# Show Policy Down Arrows
DX = -arrowSize[:, :, 1]
DY = np.zeros((self.ROWS, self.COLS))
DX = np.ma.masked_array(DX, mask=Mask[:, :, 1])
DY = np.ma.masked_array(DY, mask=Mask[:, :, 1])
C = np.ma.masked_array(arrowColors[:, :, 1], mask=Mask[:,:, 1])
self.downArrows_fig.set_UVC(DY, DX, C)
# Show Policy Left Arrows
DX = np.zeros((self.ROWS, self.COLS))
DY = -arrowSize[:, :, 2]
DX = np.ma.masked_array(DX, mask=Mask[:, :, 2])
DY = np.ma.masked_array(DY, mask=Mask[:, :, 2])
C = np.ma.masked_array(arrowColors[:, :, 2], mask=Mask[:,:, 2])
self.leftArrows_fig.set_UVC(DY, DX, C)
# Show Policy Right Arrows
DX = np.zeros((self.ROWS, self.COLS))
DY = arrowSize[:, :, 3]
DX = np.ma.masked_array(DX, mask=Mask[:, :, 3])
DY = np.ma.masked_array(DY, mask=Mask[:, :, 3])
C = np.ma.masked_array(arrowColors[:, :, 3], mask=Mask[:,:, 3])
self.rightArrows_fig.set_UVC(DY, DX, C)
plt.draw()
