def initialise(self, grid):
""" Called at the beginning of an episode. Use it to construct
the initial state.
"""
# Reset the total reward for the episode
self.total_reward = 0
cv2.imshow("Enduro", self._image)
cv2.imshow("Environment Grid", EnvironmentState.draw(grid))
def sense(self, grid):
""" Constructs the next state from sensory signals.
gird -- 2-dimensional numpy array containing the latest grid
representation of the environment
"""
# Visualise the environment grid
cv2.imshow("Environment Grid", EnvironmentState.draw(grid))
def sense(self, grid):
""" Constructs the next state from sensory signals.
gird -- 2-dimensional numpy array containing the latest grid
representation of the environment
"""
# Visualise the environment grid
cv2.imshow("Environment Grid", EnvironmentState.draw(grid))
def initialise(self, grid):
""" Called at the beginning of an episode. Use it to construct
the initial state.
"""
# Reset the total reward for the episode
self.total_reward = 0
cv2.imshow("Enduro", self._image)
cv2.imshow("Environment Grid", EnvironmentState.draw(grid))
def sense(self, grid):
self.s_ = "xxxxxxxxxx"
i = 0
for i in range(1, 9):
if grid[0][i] == 2:
i = i
break
self.s_ = str(i) + self.s_
for j in range(0, 10):
for k in range(0, 8):
if grid[k][j] == 1:
self.s_ = self.s_[:j] + str(k) + self.s_[j + 1:]
cv2.imshow("Environment Grid", EnvironmentState.draw(grid))
def sense(self, grid):
""" Constructs the next state from sensory signals.
gird -- 2-dimensional numpy array containing the latest grid
representation of the environment
"""
# Visualise the environment grid
print(grid)
# pos_i = grid[1].index(2)
print("----------------------")
print(list(grid[0]).index(2))
print("----------------------")
cv2.imshow("Environment Grid", EnvironmentState.draw(grid))
def initialise(self, grid):
""" Called at the beginning of an episode
"""
self.total_reward = 0
self.prev_state = None
self.curr_state = None
self.sense(grid)
self.last_reward = None
self.last_action = None
if not self.learning:
cv2.imshow("Enduro", self._image)
cv2.imshow("Environment Grid", EnvironmentState.draw(grid))
with open('policy.p', 'rb') as handle:
self.policy = pickle.load(handle)
def sense(self, grid):
""" Constructs the next state from sensory signals.
grid -- 2-dimensional numpy array containing the latest grid
representation of the environment
"""
index = searchState(self.history["states"], grid[:self.lookahead])
if (index == -1):
self.history["states"].append(grid[:self.lookahead])
self.history["Q"].append(np.zeros(len(self.getActionsSet())))
self.next_state = len(self.history["Q"]) - 1
else:
self.next_state = index
# Visualise the environment grid
cv2.imshow("Environment Grid", EnvironmentState.draw(grid))
def sense(self, grid):
""" Constructs the next state from sensory signals.
gird -- 2-dimensional numpy array containing the latest grid
representation of the environment
"""
# Visualise the environment grid
# m=np.where(grid==2)
# n1=m[0]
# n2=m[1]
# p1=n1[0]
# p2=n2[0]
# index_agent=np.where(grid==2)
# index_cars=np.where(grid[:8,:]==1)
#self.next_state=unicode(grid)
# self.next_state=grid.flatten()
# self.next_state=np.array2string(self.next_state)
#self.next_state=str(grid[0:2,:])
#self.check_ifstate_exist(ob_)
cv2.imshow("Environment Grid", EnvironmentState.draw(grid))
def sense(self, grid):
""" Constructs the next state from sensory signals.
gird -- 2-dimensional numpy array containing the latest grid
representation of the environment
"""
self.prev_state = self.curr_state
position2 = np.where(grid == 2)
x2 = position2[1][0]
position1 = np.where(grid == 1)
x1_1 = None
y1_1 = None
x1_2 = None
y1_2 = None
dist = None
for x, y in zip(position1[1], position1[0]):
if (dist is None) or abs(x - x2) + y < dist:
x1_1 = x
y1_1 = y
dist = max(abs(x - x2) - 1, 0) + y
x1_1 = -1 if x1_1 is None else -1 if dist > 9 or abs(x1_1 -
x2) > 2 else x1_1
dist = None
for x, y in zip(position1[1], position1[0]):
if (x != x1_1 or y != y1_1) and ((dist is None)
or abs(x - x2) + y < dist):
x1_2 = x
y1_2 = y
dist = max(abs(x - x2) - 1, 0) + y
x1_2 = -1 if x1_2 is None else -1 if dist > 9 or abs(x1_2 -
x2) > 2 else x1_2
a = 100 if x1_1 == -1 else x2 - x1_1
b = 100 if x1_2 == -1 else x2 - x1_2
self.curr_state = (a, b, x2 >= 7, x2 <= 2)
if not self.learning:
# Visualise the environment grid
cv2.imshow("Environment Grid", EnvironmentState.draw(grid))
def sense(self, grid):
self.next_state = self.buildState(grid)
# Visualise the environment grid
cv2.imshow("Environment Grid", EnvironmentState.draw(grid))
