Algorithm.py

import numpy as np 
import pylab as pl 
import networkx as nx
edges = [(0, 1), (1, 5), (5, 6), (5, 4), (1, 2),  
         (1, 3), (9, 10), (2, 4), (0, 6), (6, 7), 
         (8, 9), (7, 8), (1, 7), (3, 9)] 
  
goal = 10
G = nx.Graph() 
G.add_edges_from(edges) 
pos = nx.spring_layout(G) 
nx.draw_networkx_nodes(G, pos) 
nx.draw_networkx_edges(G, pos) 
nx.draw_networkx_labels(G, pos) 
pl.show() 
MATRIX_SIZE = 11
M = np.matrix(np.ones(shape =(MATRIX_SIZE, MATRIX_SIZE))) 
M *= -1
  
for point in edges: 
    print(point) 
    if point[1] == goal: 
        M[point] = 100
    else: 
        M[point] = 0
  
    if point[0] == goal: 
        M[point[::-1]] = 100
    else: 
        M[point[::-1]]= 0
        # reverse of point 
  
M[goal, goal]= 100
print(M) 
# add goal point round trip
Q = np.matrix(np.zeros([MATRIX_SIZE, MATRIX_SIZE])) 
  
gamma = 0.75
# learning parameter 
initial_state = 1
  
# Determines the available actions for a given state 
def available_actions(state): 
    current_state_row = M[state, ] 
    available_action = np.where(current_state_row >= 0)[1] 
    return available_action 
  
available_action = available_actions(initial_state) 
  
# Chooses one of the available actions at random 
def sample_next_action(available_actions_range): 
    next_action = int(np.random.choice(available_action, 1)) 
    return next_action 
  
  
action = sample_next_action(available_action) 
  
def update(current_state, action, gamma): 
  
  max_index = np.where(Q[action, ] == np.max(Q[action, ]))[1] 
  if max_index.shape[0] > 1: 
      max_index = int(np.random.choice(max_index, size = 1)) 
  else: 
      max_index = int(max_index) 
  max_value = Q[action, max_index] 
  Q[current_state, action] = M[current_state, action] + gamma * max_value 
  if (np.max(Q) > 0): 
    return(np.sum(Q / np.max(Q)*100)) 
  else: 
    return (0) 
# Updates the Q-Matrix according to the path chosen 
  
update(initial_state, action, gamma) 
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brightness_4
scores = [] 
for i in range(1000): 
    current_state = np.random.randint(0, int(Q.shape[0])) 
    available_action = available_actions(current_state) 
    action = sample_next_action(available_action) 
    score = update(current_state, action, gamma) 
    scores.append(score) 
  
# print("Trained Q matrix:") 
# print(Q / np.max(Q)*100) 
# You can uncomment the above two lines to view the trained Q matrix 
  
# Testing 
current_state = 0
steps = [current_state] 
  
while current_state != 10: 
  
    next_step_index = np.where(Q[current_state, ] == np.max(Q[current_state, ]))[1] 
    if next_step_index.shape[0] > 1: 
        next_step_index = int(np.random.choice(next_step_index, size = 1)) 
    else: 
        next_step_index = int(next_step_index) 
    steps.append(next_step_index) 
    current_state = next_step_index 
  
print("Most efficient path:") 
print(steps) 
  
pl.plot(scores) 
pl.xlabel('No of iterations') 
pl.ylabel('Reward gained') 
pl.show() 
# Defining the locations of the police and the drug traces 
police = [2, 4, 5] 
drug_traces = [3, 8, 9] 
  
G = nx.Graph() 
G.add_edges_from(edges) 
mapping = {0:'0 - Detective', 1:'1', 2:'2 - Police', 3:'3 - Drug traces', 
           4:'4 - Police', 5:'5 - Police', 6:'6', 7:'7', 8:'Drug traces', 
           9:'9 - Drug traces', 10:'10 - Drug racket location'} 
  
H = nx.relabel_nodes(G, mapping) 
pos = nx.spring_layout(H) 
nx.draw_networkx_nodes(H, pos, node_size =[200, 200, 200, 200, 200, 200, 200, 200]) 
nx.draw_networkx_edges(H, pos) 
nx.draw_networkx_labels(H, pos) 
pl.show() 
Q = np.matrix(np.zeros([MATRIX_SIZE, MATRIX_SIZE])) 
env_police = np.matrix(np.zeros([MATRIX_SIZE, MATRIX_SIZE])) 
env_drugs = np.matrix(np.zeros([MATRIX_SIZE, MATRIX_SIZE])) 
initial_state = 1
  
# Same as above 
def available_actions(state): 
    current_state_row = M[state, ] 
    av_action = np.where(current_state_row >= 0)[1] 
    return av_action 
  
# Same as above 
def sample_next_action(available_actions_range): 
    next_action = int(np.random.choice(available_action, 1)) 
    return next_action 
  
# Exploring the environment 
def collect_environmental_data(action): 
    found = [] 
    if action in police: 
        found.append('p') 
    if action in drug_traces: 
        found.append('d') 
    return (found) 
  
   
available_action = available_actions(initial_state) 
action = sample_next_action(available_action) 
  
def update(current_state, action, gamma): 
  max_index = np.where(Q[action, ] == np.max(Q[action, ]))[1] 
  if max_index.shape[0] > 1: 
      max_index = int(np.random.choice(max_index, size = 1)) 
  else: 
      max_index = int(max_index) 
  max_value = Q[action, max_index] 
  Q[current_state, action] = M[current_state, action] + gamma * max_value 
  environment = collect_environmental_data(action) 
  if 'p' in environment: 
    env_police[current_state, action] += 1
  if 'd' in environment: 
    env_drugs[current_state, action] += 1
  if (np.max(Q) > 0): 
    return(np.sum(Q / np.max(Q)*100)) 
  else: 
    return (0) 
# Same as above 
update(initial_state, action, gamma) 
  
def available_actions_with_env_help(state): 
    current_state_row = M[state, ] 
    av_action = np.where(current_state_row >= 0)[1] 
  
    # if there are multiple routes, dis-favor anything negative 
    env_pos_row = env_matrix_snap[state, av_action] 
  
    if (np.sum(env_pos_row < 0)): 
        # can we remove the negative directions from av_act? 
        temp_av_action = av_action[np.array(env_pos_row)[0]>= 0] 
        if len(temp_av_action) > 0: 
            av_action = temp_av_action 
    return av_action 
# Determines the available actions according to the environment 
Step 8: Visualising the Environmental matrices

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scores = [] 
for i in range(1000): 
    current_state = np.random.randint(0, int(Q.shape[0])) 
    available_action = available_actions(current_state) 
    action = sample_next_action(available_action) 
    score = update(current_state, action, gamma) 
  
# print environmental matrices 
print('Police Found') 
print(env_police) 
print('') 
print('Drug traces Found') 
print(env_drugs)
scores = [] 
for i in range(1000): 
    current_state = np.random.randint(0, int(Q.shape[0])) 
    available_action = available_actions_with_env_help(current_state) 
    action = sample_next_action(available_action) 
    score = update(current_state, action, gamma) 
    scores.append(score) 
  
pl.plot(scores) 
pl.xlabel('Number of iterations') 
pl.ylabel('Reward gained') 
pl.show()