best_a = np.argmax(
self.Q[self.agent.convert_location_to_state(), :])
if random_step:
a = np.random.choice(self.nA)
else:
a = best_a
r = self.get_reward(a)
best_r = self.get_reward(best_a)
new_belief = self.Q[self.agent.convert_location_to_state(),a] + \
alpha * (r + gamma * best_r
- self.Q[self.agent.convert_location_to_state(),a])
self.Q[self.agent.convert_location_to_state(), a] = new_belief
self.agent.simulate_step(a)
def get_reward(self, a):
return self.agent.get_reward(a) + self.grid.get_reward()[0]
def choose_action(self):
return np.argmax(self.Q[self.agent.convert_location_to_state(), :])
if __name__ == '__main__':
grid = Grid()
grid.add_agent()
grid.add_agent()
ql = QLearning(grid.agents[0])
print(ql.choose_action())
ql.learn()
print(ql.choose_action())
def RECONNECT(self):
self.Gr = nx.DiGraph()
for i, agent in enumerate(self.grid.agents):
self.Gr.add_node(agent, label=i)
self.D = self.get_Dist_Mat()
self.connect(self.D)
def show_Graph(self):
nx.draw(Gr.Gr)
plt.show()
if __name__ == '__main__':
G = Grid()
G.add_agent([1, 0], viewing=3)
G.add_agent([3, 0], viewing=3.3)
G.add_agent([3, 3])
Gr = Graph(G)
Gr.show_Graph()
# from Grid import Grid
# import numpy as np
# G = Grid()
# G.add_agent([1,0],viewing = 2)
# G.add_agent([3,0])
#
# Gr = nx.Graph()
# for i,agent in enumerate(G.agents):
# Gr.add_node(agent)
# vecs = []
class Model:
'''
Model class for the Schelling segregation model.
'''
def __init__(self, width, height, density, similarity):
self.width = width
self.height = height
num_agent = (int)(height * width * density /
2) # so agent moi loai, chua ra 20% empty cell
self.similarity = similarity # muc do tuong tu
self.schedule = RandomActivation(self)
self.grid = Grid(height, width)
self.happy = 0
self.running = True
# Set up agents
id = 0
id = self._create_agent(id, num_agent, Agent.typeA)
id = self._create_agent(id, num_agent, Agent.typeB)
self.grid.cal_happiness() # tinh initial happiness
def _create_agent(self, startid, num, type):
id = startid
for i in range(0, num):
agent = Agent(id, (0, 0), self, type)
id = id + 1
self.grid.add_agent(agent)
self.schedule.add(agent)
return id
def step(self):
'''
Run one step of the model. If All agents are happy, halt the model.
'''
self.happy = 0 # Reset counter of happy agents
self.schedule.step()
if self.happy == self.schedule.get_agent_count():
self.running = False
def plot_grid(self, savefile=False, filename=None):
self.grid.plot_grid(self.happy, savefile, filename)
def plot_happiness(self, filename):
self.grid.plot_happiness(self.happy, filename)
def is_happy(self): # model da happy roi thi dung lai
return not self.running
# it's just for test purpose
def print_grid(self):
for cell in self.grid.coord_iter():
agent = cell[0]
y = cell[2]
if agent == None:
agent_type = -1
else:
agent_type = agent.type
print '{} '.format(agent_type),
if y == self.width - 1:
print('\n')
Map[1, 4] = 100000
Map[2, 4] = 100000
Map[3, 4] = 100000
Map[4, 4] = 100000
Map[5, 4] = 100000
Map[6, 4] = 100000
Map[7, 4] = 100000
Map[8, 3] = 100000
Map[17, 17] = 100000
Map[17, 18] = 100000
Map[18, 17] = 100000
Map[18, 16] = 100000
Map[16, 18] = 100000
grid = Grid(Map=Map, meeting_point=[18, 18])
plt.figure(1)
grid.show_grid()
plt.figure(2)
for _ in range(30):
grid.add_agent(location=[3, 1],
learner='DynamicMCTSFinder',
ant_mode=False)
# grid.add_agent(location=[3, 1], learner='DynamicMCTSFinder',ant_mode = False)
# grid.add_agent(location=[8,7],learner = 'DynamicMCTSFinder')
#grid.add_agent(learner='RandomFinder')
#grid.add_agent(learner='RandomFinder')
#grid.add_agent(learner='RandomFinder')
print('meeting point is %s' % (str(grid.agents[0].meeting_point)))
#Simulatortext(grid, num_steps=100)
Simulatorgraphical(grid, num_steps=100, just_agents=True)
# a = np.argmax(policy[s0])
# if Q[s0,a]==0:
# for action in agent
def MCTS(agent, env, depth, gamma=.8):
Q = np.zeros((agent.nS, agent.nA))
s = agent.convert_location_to_state()
for a in agent.nA:
s_, r = agent.simulate(action)
Q[s, a] = r + gamma * MCTS
if __name__ == '__main__':
grid = Grid(n=5)
grid.add_agent(location=(3, 3))
grid.add_agent(location=(3, 4))
done = False
while not done:
for agent in grid.agents:
print(agent.location)
print(agent.last_action)
print('-----------')
actions = input('actions:')
R, done = grid.time_step(actions=actions)
Reward = R
#done = done
grid.show_grid()
for agent in grid.agents:
print(agent.convert_location_to_state())
print(grid.get_reward())
