def test_acer():
def Sphere(individual):
"""Sphere test objective function.
F(x) = sum_{i=1}^d xi^2
d=1,2,3,...
Range: [-100,100]
Minima: 0
"""
return sum(x**2 for x in individual)
nx=5
bounds={}
for i in range(1,nx+1):
bounds['x'+str(i)]=['int', -100, 100]
#create an enviroment class
env=CreateEnvironment(method='acer', fit=Sphere,
bounds=bounds, mode='min', episode_length=50)
#create a callback function to log data
cb=RLLogger(check_freq=1, mode='min')
#create an acer object based on the env object
acer = ACER(MlpPolicy, env=env, n_steps=25, q_coef=0.55, ent_coef=0.02)
#optimise the enviroment class
acer.learn(total_timesteps=2000, callback=cb)
#print the best results
print('--------------- ACER results ---------------')
print('The best value of x found:', cb.xbest)
print('The best value of y found:', cb.rbest)
return
def Sphere(individual):
"""Sphere test objective function.
F(x) = sum_{i=1}^d xi^2
d=1,2,3,...
Range: [-100,100]
Minima: 0
"""
return sum(x**2 for x in individual)
nx = 5
bounds = {}
for i in range(1, nx + 1):
bounds['x' + str(i)] = ['float', -10, 10]
#create an enviroment class
env = CreateEnvironment(method='acktr',
fit=Sphere,
bounds=bounds,
mode='min',
episode_length=50)
#create a callback function to log data
cb = RLLogger(check_freq=1, mode='min')
#create an acktr object based on the env object
acktr = ACKTR(MlpPolicy, env=env, n_steps=12, seed=1)
#optimise the enviroment class
acktr.learn(total_timesteps=2000, callback=cb)
#print the best results
print('--------------- ACKTR results ---------------')
print('The best value of x found:', cb.xbest)
print('The best value of y found:', cb.rbest)
p.add_reporter(stats)
p.add_reporter(neat.Checkpointer(10))
winner = p.run(self.eval_genomes, ngen)
with open('winner.pkl', 'wb') as output:
pickle.dump(winner, output, 1)
return self.best_x, self.best_fit_correct, self.history
nx = 10
bounds = {}
for i in range(1, nx + 1):
bounds['x' + str(i)] = ['float', -100, 100]
#create an enviroment class
env = CreateEnvironment(method='neat',
fit=Sphere,
bounds=bounds,
mode='max',
episode_length=50)
neats = NEAT(env=env, config='config-feedforward')
x_best, y_best, neat_hist = neats.evolute(ngen=60)
assert Sphere(x_best) == y_best
print(x_best, y_best)
import matplotlib.pyplot as plt
plt.figure()
plt.plot(neat_hist['global_fitness'])
#plt.plot(neat_hist['local_fitness'])
#---------------------------------
bounds = {}
bounds['x1'] = ['int', 1, 99]
bounds['x2'] = [
'grid',
(0.0625, 0.125, 0.1875, 0.25, 0.3125, 0.375, 0.4375, 0.5, 0.5625, 0.625)
]
bounds['x3'] = ['float', 10, 200]
bounds['x4'] = ['float', 10, 200]
#---------------------------------
# PPO
#---------------------------------
env = CreateEnvironment(method='ppo',
fit=Vessel,
bounds=bounds,
mode='min',
episode_length=50)
cb = RLLogger(check_freq=1, mode='min')
ppo = PPO2(policy=MlpPolicy, env=env, n_steps=20, seed=1)
ppo.learn(total_timesteps=1000, callback=cb)
print('--------------- PPO results ---------------')
print('The best value of x found:', cb.xbest)
print('The best value of y found:', cb.rbest)
assert Vessel(cb.xbest) - cb.rbest < 1e-3
#---------------------------------
# A2C
#---------------------------------
cb = RLLogger(check_freq=1, mode='min')