import os
import pickle
import neat
import gym
import numpy as np

with open('winner', 'rb') as f:
    c = pickle.load(f)

print('Loaded genome:')
print(c)

local_dir = os.path.dirname(__file__)
config_path = os.path.join(local_dir, 'config')
config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction, 
                    neat.DefaultSpeciesSet, neat.DefaultStagnation,
                    config_path)

net = neat.nn.FeedForwardNetwork.create(c, config)

env = gym.make("BipedalWalker-v3")
observation = env.reset()


done = False
while not done:
    action = net.activate(observation) 

    observation, reward, done, info = env.step(action)
    env.render()

c78a177f50f85f35e0217916d3773b347b4cb2f7
<mask token>

<mask token>
with open('winner', 'rb') as f:
    c = pickle.load(f)
print('Loaded genome:')
print(c)
<mask token>
while not done:
    action = net.activate(observation)
    observation, reward, done, info = env.step(action)
    env.render()

<mask token>
with open('winner', 'rb') as f:
    c = pickle.load(f)
print('Loaded genome:')
print(c)
local_dir = os.path.dirname(__file__)
config_path = os.path.join(local_dir, 'config')
config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction, neat.
    DefaultSpeciesSet, neat.DefaultStagnation, config_path)
net = neat.nn.FeedForwardNetwork.create(c, config)
env = gym.make('BipedalWalker-v3')
observation = env.reset()
done = False
while not done:
    action = net.activate(observation)
    observation, reward, done, info = env.step(action)
    env.render()

import os
import pickle
import neat
import gym
import numpy as np
with open('winner', 'rb') as f:
    c = pickle.load(f)
print('Loaded genome:')
print(c)
local_dir = os.path.dirname(__file__)
config_path = os.path.join(local_dir, 'config')
config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction, neat.
    DefaultSpeciesSet, neat.DefaultStagnation, config_path)
net = neat.nn.FeedForwardNetwork.create(c, config)
env = gym.make('BipedalWalker-v3')
observation = env.reset()
done = False
while not done:
    action = net.activate(observation)
    observation, reward, done, info = env.step(action)
    env.render()

import os
import pickle
import neat
import gym
import numpy as np

with open('winner', 'rb') as f:
    c = pickle.load(f)

print('Loaded genome:')
print(c)

local_dir = os.path.dirname(__file__)
config_path = os.path.join(local_dir, 'config')
config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction, 
                    neat.DefaultSpeciesSet, neat.DefaultStagnation,
                    config_path)

net = neat.nn.FeedForwardNetwork.create(c, config)

env = gym.make("BipedalWalker-v3")
observation = env.reset()


done = False
while not done:
    action = net.activate(observation) 

    observation, reward, done, info = env.step(action)
    env.render()
