巧妙地解决问题的最佳方案不是支配地位

Question

我正在尝试使用整洁的方法解决openaigym的沃克问题

问题在下面 https://gym.openai.com/envs/BipedalWalker-v2/

我尝试使用整洁的python解决此问题，但是即使经过1000代，解决方案仍未解决。 我该怎么办？

avg_fitness

特殊化

图

from __future__ import print_function
import os
import neat
import visualize
import gym
import numpy as np
from gym import wrappers

env = gym.make("BipedalWalker-v2")

def eval_genomes(genomes, config):
    global env
    global MAX_STEPS

    for genome_id, genome in genomes:
        genome.fitness = 0
        net = neat.nn.FeedForwardNetwork.create(genome, config)
        observation = env.reset()
        total_reward = 0
        for _ in range(1):
            observatin = env.reset()
            episode_reward = 0
            while True:
                action = net.activate(observation)
                action = np.clip(action,-1,1)
                observation,reward,done,info = env.step(action)
                episode_reward += reward
                if done:
                    total_reward += episode_reward
                    break
        genome.fitness = total_reward / 1

def run(config_file):
    global env
    # Load configuration.
    config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction,
                     neat.DefaultSpeciesSet, neat.DefaultStagnation,
                     config_file)

    # Create the population, which is the top-level object for a NEAT run.
    p = neat.Population(config)
    # Add a stdout reporter to show progress in the terminal.
    p.add_reporter(neat.StdOutReporter(True))
    stats = neat.StatisticsReporter()
    p.add_reporter(stats)
    p.add_reporter(neat.Checkpointer(5))

    # Run for up to 300 generations.

    winner = p.run(eval_genomes, 200)


    # Display the winning genome.
    print('\nBest genome:\n{!s}'.format(winner))

    # Show output of the most
    #fit genome against training data.
    print('\nOutput:')
    #winner_net = neat.nn.FeedForwardNetwork.create(winner, config)
    visualize.draw_net(config, winner, True,)
    visualize.plot_stats(stats, ylog=False, view=True)
    visualize.plot_species(stats, view=True)
    winner_net = neat.nn.FeedForwardNetwork.create(winner, config)



if __name__ == '__main__':
# Determine path to configuration file. This path manipulation is
# here so that the script will run successfully regardless of the
# current working directory.

    local_dir = os.path.dirname(__file__)
    config_path = os.path.join(local_dir, 'config-feedforward')
    run(config_path)

这是一个超级参数

fitness_criterion =平均值

fitness_threshold = 300

pop_size = 100

reset_on_extinction =真

no_fitness_termination = 1

activation_default = relu

activation_mutate_rate = 0.3

activation_options =乙状结肠

aggregation_default =总和

aggregation_mutate_rate = 0.3

aggregation_options =平均值

bias_init_mean = 0

bias_init_stdev = 1.0

bias_max_value = 100

bias_min_value = -100

bias_mutate_power = 0.5

bias_mutate_rate = 0.7

bias_replace_rate = 0.1

compatibility_disjoint_coefficient = 1.0

compatibility_weight_coefficient = 0.5

conn_add_prob = 0.5

conn_delete_prob = 0.5

enabled_default = True

enabled_mutate_rate = 0.01

feed_forward =真

initial_connection =已满

node_add_prob = 0.3

node_delete_prob = 0.3

num_hidden = 0

num_inputs = 24

num_outputs = 4

response_init_mean = 2

response_init_stdev = 1

response_max_value = 5.0

response_min_value = -5.0

response_mutate_power = 0.0

response_mutate_rate = 0.0

response_replace_rate = 0.0

weight_init_mean = 0.5

weight_init_stdev = 1.0

weight_max_value = 30

weight_min_value = -30

weight_mutate_power = 0.5

weight_mutate_rate = 0.8

weight_replace_rate = 0.1

compatibility_threshold = 3.0

species_fitness_func =平均值

max_stagnation = 20

species_elitism = 3

精英= 3

survival_threshold = 0.3