def __init__(self, m, n):
'''
'''
self.env = GridWorld(m, n)
self.q = self.init_q()
self.current = [1, 0]
self.gamma = 0.9
def calculate_step(self, x, y, policy):
gamma = 1
state = np.array([x, y])
forward_step = GridWorld.step(state, policy[x, y])
if (policy[x, y] == 'u' or policy[x, y] == 'd'):
side_step_one = GridWorld.step(state, 'l')
side_step_two = GridWorld.step(state, 'r')
elif (policy[x, y] == 'l' or policy[x, y] == 'r'):
side_step_one = GridWorld.step(state, 'u')
side_step_two = GridWorld.step(state, 'd')
reward = -0.04 + gamma * (0.8 * self.world[forward_step] +
0.1 * self.world[side_step_one] +
0.1 * self.world[side_step_two])
return reward
class mdp:
def __init__ (self, path):
self.world = GridWorld(path)
self.directions = self.world.get_directions()
def create_policy(self):
#TODO
directionsTranslator = {0:'u', 1: 'd', 2: 'l', 3: 'r'}
policy = np.random.randint(0,len(self.directions),self.world.get_size())
outPol = np.chararray(self.world.get_size())
for i in range(0,policy.max()+1):
outPol[policy == i] = directionsTranslator[i]
outPol[self.world.free_map()] = 'X'
return outPol.decode('UTF-8')
def calculate_step(self,x,y,policy):
gamma = 1
state = np.array([x,y])
forward_step = self.world.step(state,policy[x,y])
if(policy[x,y] == 'u' or policy[x,y] == 'd'):
side_step_one = self.world.step(state,'l')
side_step_two = self.world.step(state,'r')
elif(policy[x,y] == 'l' or policy[x,y] == 'r'):
side_step_one = self.world.step(state,'u')
side_step_two = self.world.step(state,'d')
reward = -0.04 +gamma*(0.8* self.world[forward_step]+0.1*self.world[side_step_one]+0.1*self.world[side_step_two])
return reward
def evaluate_policy(self,policy, max_steps):
evaluated_policy = np.zeros(shape = policy.shape)
n = 0
while(n < max_steps):
M = self.world.get_size()
print(M)
for x in range(M[0]):
for y in range(M[1]):
evaluated_policy[x,y] = mdp.calculate_step(self,x,y,policy)
n += 1
return evaluated_policy
def iterate_policy(self):
#TODO
a=5
def print_solutions(self):
#TODO
a=5
class QLearning(object):
'''
'''
def __init__(self, m, n):
'''
'''
self.env = GridWorld(m, n)
self.q = self.init_q()
self.current = [1, 0]
self.gamma = 0.9
def init_q(self):
'''
:return:
'''
q = dict()
for i in range(self.env.grid.shape[0]):
q[i] = {}
for j in range(self.env.grid.shape[1]):
q[i][j] = {}
for a in self.env.actions:
q[i][j][a] = 0
return q
def learn(self):
'''
:return:
'''
for i in range(500):
a = np.random.choice(self.env.actions.__len__(), 1)[0]
next, reward = self.env.step(self.current, a)
self.q[self.current[0]][
self.current[1]][a] = reward + self.gamma * max(
self.q[next[0]][next[1]].values())
self.current = next
def create_policy(self):
#TODO
directions = GridWorld.getSize(self.world)
print(directions)
def __init__(self, path):
self.world = GridWorld(path)
def __init__ (self, path):
self.world = GridWorld(path)
self.directions = self.world.get_directions()