def load(self, s):
""" Load the Chromosome from
save passed as parameter.
"""
self._size = s["size"]
self._gene_count = s["gene_count"]
self._genes = []
for g in s["genes"]:
new_gene = Gene(self._size)
new_gene.load(g)
self._genes.append(new_gene)
return s["generation"]
from gene import Gene
import gym, numpy as np
env = gym.make('CartPole-v0')
input_shape = env.observation_space.shape[0]
output_shape = env.action_space.n
def demo(gene):
done = False
state = env.reset()
total_reward = 0.0
while not done:
env.render()
action = gene.action(state[np.newaxis, :])[0]
state, reward, done, info = env.step(action)
total_reward += reward
print(total_reward)
print('How will it do?')
agent = Gene(input_shape, [10, 5], output_shape)
agent.load("champ.p")
demo(agent)