hi, can this train some fighting games such as the epic Street Fight 2 ?

[drizzt00s]

Issue summary

[Put a detailed description of the issue here.]

System information

• [Operating system]
• [Python version]
• [Gym Retro version]

[Bigous]

Yes. Look at this.

[troller08]

bro take this
import tensorflow as tf
import numpy as np
import cv2
import neat
import pickle
import retro
import os

Prompt the user to enter the game and level information

game_name = input("Enter the game name: ")
level_name = input("Enter the level name: ")

env = retro.make(game_name, level_name)

Define a function to preprocess the game frames

def preprocess_frame(frame):
# Resize the frame
frame = cv2.resize(frame, (int(frame.shape[1]/8), int(frame.shape[0]/8)))
# Convert the frame to grayscale
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Reshape the frame
frame = np.reshape(frame, (frame.shape[0], frame.shape[1], 1))
return frame

def check_stuck(action_history, action_count_threshold):
# Check if the AI has taken the same action multiple times
action_counts = np.unique(action_history, return_counts=True)[1]
if action_counts.size == 0:
return False
elif action_counts.max() >= action_count_threshold:
return True
else:
return False

def eval_genomes(genomes, config):
for genome_id, genome in genomes:
ob = env.reset()
ac = env.action_space.sample()

    inx, iny, inc = env.observation_space.shape

    inx = int(inx/8)
    iny = int(iny/8)

    net = neat.nn.recurrent.RecurrentNetwork.create(genome, config)
    
    current_max_fitness = 0
    fitness_current = 0
    frame = 0
    counter = 0
    xpos = 0
    xpos_max = 0
    action_history = []
    
    done = False
    
    while not done:
        env.render()
        frame += 1
        ob = preprocess_frame(ob)
        imgarray = np.ndarray.flatten(ob)
        nnOutput = net.activate(imgarray.astype(np.float32))
        ob, rew, done, info = env.step(nnOutput)
        
        fitness_current += rew
        
        # Penalize the fitness score if the AI is stuck in a particular position
        if check_stuck(action_history, 30):
            fitness_current -= 10
            
        action_history.append(np.argmax(nnOutput))
        
        if fitness_current > current_max_fitness:
            current_max_fitness = fitness_current
            counter = 0
        else:
            counter += 1
            
        if counter == 250:
            done = True
            
        if done:
            done = True
            print(genome_id, fitness_current)
            
        genome.fitness = fitness_current
        
        # Save the progress of the AI every 10 generations
        if genome_id % 100 == 0:
            with open(f'neat-checkpoint-{genome_id}.pkl', 'wb') as output:
                pickle.dump((p, stats, genome), output, 1)
            
print(frame)     

Load the saved progress of the AI

if os.path.exists('neat-checkpoint'):
p = neat.Checkpointer.restore_checkpoint('neat-checkpoint')
else:
config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction,
neat.DefaultSpeciesSet, neat.DefaultStagnation,
'config-feedforward')
p = neat.Population(config)

p.add_reporter(neat.StdOutReporter(True))
stats = neat.StatisticsReporter()
p.add_reporter(stats)
p.add_reporter(neat.Checkpointer(10, filename_prefix='neat-checkpoint-'))

winner = p.run(eval_genomes)

p.add_reporter(neat.StdOutReporter(True))
stats = neat.StatisticsReporter()
p.add_reporter(stats)
p.add_reporter(neat.Checkpointer(10, filename_prefix='neat-checkpoint-'))

winner = p.run(eval_genomes)

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

cv2.destroyAllWindows()