AI_bot_image.py

import gym
import numpy as np
import cv2
import time
import neat
from inputgenerator import inputgen
from visualize import plot_species, draw_net, plot_stats
import pickle

env = gym.make('SpaceInvaders-v0')
#To record in particular episodes
env = gym.wrappers.Monitor(env, "recording_image",video_callable=lambda episode_id: episode_id%50==0)
#Size of the game box
print(env.observation_space)
print(env.unwrapped.get_action_meanings())
action_name = {0:'NOOP', 1:'FIRE', 2:'RIGHT', 3:'LEFT', 4:'RIGHTFIRE', 5:'LEFTFIRE'}

#parameters (priors) we got from analyzing images for faster computation
starting_pixel = 114
self_y = 192
search_x_start = 22
search_x_end = 139
rock_color = 107

def eval_genomes(genomes,config):
#evalutes genomes based on their fitness scores
    for genome_id,genome in genomes:
        observation = env.reset()
        high_score = 0
        frame = 0
        my_pos_currrent = 0
        counter = 0
        #creates a feed forward network from the genome
        net = neat.nn.feed_forward.FeedForwardNetwork.create(genome,config)
        #Simulation starts
        while True:
            frame += 1
            env.render()
            #Processing observation image to get our set of inputs
            img = cv2.cvtColor(observation, cv2.COLOR_BGR2RGB)
            imp_values = inputgen(img)
            img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
            #Extracting basic info that will be used for fitness
            aliens_dead = imp_values[-1]
            my_pos = imp_values[0]

            #For extending the bullet to the ground or rock , whichever comes first
            x,y = imp_values[-3],imp_values[-2]
            #For finding whether rock comes in closest bullets trajectory
            loc = np.where(img[:,x]==rock_color)
            if len(loc[0])==0:
                img[y:,x] = 200
            else:
                img[y:loc[0][0],x] = 200
            #Giving input as image
            ob = img[starting_pixel:self_y+2,search_x_start-5:search_x_end+5]
            h_ob,w_ob = ob.shape #184,127
            #Sizing it down for faster network decision making, big input takes more time to evaluate
            inx = int(h_ob/3)
            iny = int(w_ob/3)
            ob = cv2.resize(ob, (iny, inx))
            ob = np.reshape(ob, (inx,iny))
            imgarray = np.ndarray.flatten(ob)

            ai_decision = net.activate(imgarray)
            action = np.argmax(ai_decision)
            observation, reward, done, info = env.step(action)
            #for the AI to learn how to dodge the bullet
            if my_pos==x:
                penalty = -10
            else:
                penalty = 0
            if my_pos!=my_pos_currrent:
                counter+=1
            else:
                counter=0
            my_pos_currrent = my_pos
            high_score += reward
            if done:
                break
        fitness = 0.8*high_score + 0.01*frame + 11*aliens_dead + 0.5*counter + penalty
        print(genome_id,fitness,aliens_dead)
        if fitness==None:
            fitness =0
        #To immediately stop once it kills all enemies achieving its goal
        if aliens_dead==36:
            fitness = 3000 #More than stopping threshold
        genome.fitness = fitness
# Setting the configuration
config = neat.Config(neat.DefaultGenome,neat.DefaultReproduction,
                    neat.DefaultSpeciesSet,neat.DefaultStagnation,'config')

#Initializing the population class
p = neat.Population(config)
# p = neat.Checkpointer.restore_checkpoint('./saved_models_image/neat-checkpoint-116')

#For statistics
p.add_reporter(neat.StdOutReporter(True))
stats = neat.StatisticsReporter()
p.add_reporter(stats)
p.add_reporter(neat.Checkpointer(100,filename_prefix='./saved_models_image/neat-checkpoint-'))

#Returns the genotype or network specifics
best_bot = p.run(eval_genomes)
env.close()
#plots graphs
draw_net(config, best_bot,view=False,filename='./plots_image/nets2.svg')
plot_stats(stats, ylog=False, view=False,filename='./plots_image/avg_fitness1.svg')
plot_species(stats, view=False,filename='./plots_image/speciation1.svg')

with open('best_bot.pkl', 'wb') as output:
    pickle.dump(best_bot, output, 1)

print(best_bot)