MountainCar with NEAT

MountainCar with NEAT

from __future__ import print_function

import gym
import numpy as np
import itertools
import os

from neat import nn, population, statistics

# another great example of how crazy effective NEAT can be even when it knows literally nothing about what's going on in the environment.
# of course, it's all luck in hitting those super fit organisms, so it's best to have a good processor

# also, I spent more time tuning the parameters for evolution and the neural net than I spent writing this code. that's the real challenge with neat.

np.set_printoptions(threshold=np.inf)
env = gym.make('MountainCar-v0')

# run through the population

def eval_fitness(genomes):
	for g in genomes:
		observation = env.reset()
		env.render()
		net = nn.create_feed_forward_phenotype(g)

		frames = 0

		fitness = 0
		
		while 1:
			inputs = observation
			output = net.serial_activate(inputs)

			action = round(output[0]) + 1
			observation, reward, done, info = env.step(action)

			env.render()
			frames += 1

			if (frames >= 500):
				done = True
			if done:
				fitness = -(frames**2)
				print(fitness, frames)
				env.reset()
				break
		# evaluate the fitness
		g.fitness = fitness

env.monitor.start('gym_results/MountainCar-v0-experiment-1',force = True)

local_dir = os.path.dirname(__file__)
config_path = os.path.join(local_dir, 'mountainCar_config')

pop = population.Population(config_path)
pop.run(eval_fitness, 300)

winner = pop.statistics.best_genome()
print("Winner found with fitness of ", winner.fitness)
print("Fitness is novel to this solution. Now testing consecutative trials")

streak = 0

observation = env.reset()
env.render()
winningnet = nn.create_feed_forward_phenotype(winner)

while (streak < 100):
	frames = 0

	fitness = 0

	while 1:
		inputs = observation
		output = winningnet.serial_activate(inputs)

		action = round(output[0]) + 1
		observation, reward, done, info = env.step(action)

		env.render()
		frames += 1

		if (frames >= 100):
			streak = 0
			done = True
		if done:
			streak += 1
			print("streak: ", streak)
			fitness = -(frames**2)
			print(fitness, frames)
			env.reset()
			break

env.monitor.close()
gym.upload('gym_results/MountainCar-v0-experiment-1', api_key='sk_LhbQ5JJASK6f8ZJqHWBbGw')

Config file is here:
https://gist.github.com/StuartFarmer/0eb392275e9628498a3d25e6d5fb7d42

The gif and solve time do not look right, compare to some other solutions, are you sure you were outputting valid actions?