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lunarlander_es.py
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lunarlander_es.py
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# Trains the lunar lander problem using ES
import es
import nn
import numpy as np
import gym
import multiprocessing as mp
import sys
env = gym.make('LunarLanderContinuous-v2')
# define network architecture
x = i = nn.Input((8,))
x = nn.Dense(2)(x)
net = nn.Model(i, x)
del x, i
# vectorized weights and original shape information
outw, outs = nn.get_vectorized_weights(net)
# run lunar lander problem
def fitness_lander(w, render: bool=False, steps=1000):
score = 0
nn.set_vectorized_weights(net, w, outs)
n = 10
if render:
n = 3
for _ in range(n):
env._max_episode_steps = steps
obs = env.reset()
# total reward (fitness score)
s = 0
while True:
close = False
if render:
close = not env.render()
# print(obs)
# determine action to take
res = net.predict(np.expand_dims(obs, 0))[0]
res = res * 2 - 1
action = res #np.argmax(res)
obs, reward, done, _ = env.step(action)
s += reward
if done or close:
break
score += s
if render:
print(s)
env.close()
if close:
break
env.close()
return score / n
if __name__ == "__main__":
# init ES
e = es.EvolutionStrategy(
outw,
5.0,
300,
10,
min_sigma=1e-3,
big_sigma=5e-2,
wait_iter=5
)
# multiprocessing
pool = mp.Pool()
LENGTH = 1000
times = 0
best = 0 #-float('inf')
try:
for i in range(1000):
scores = []
pop = e.ask()
# eval population
for ind in pop:
scores.append(pool.apply_async(fitness_lander, ((ind, False, LENGTH))))
thread_scores = scores
scores = []
ii = 0
for s in thread_scores:
scores.append(s.get())
ii += 1
print("{} / {}".format(ii, len(thread_scores)), end='\r')
# scores = [s.get() for s in scores]
e.tell(scores)
max_score = np.max(scores)
if True:
if max_score > best:
best = max_score
# show best individual
ind = pop[np.argmax(scores)]
fitness_lander(ind, render=True)
except Exception as e:
print("Error while training:", e)