def fitness(individual):
env = self.env
adapters = self.adapters
total_reward = 0
trials = self.trials_per_individual
action_prob = self.action_prob
action_valid = False
action = None
for _ in range(trials):
observation = env.reset()
ctx = hg.ExecutionContext(tweaks=individual)
with ctx.as_default():
for t in range(self.max_steps):
if (not action_valid
) or action_prob == 1 or np.random.uniform(
) <= action_prob:
action = graph(
input=adapters[0].from_gym(observation))
action = adapters[1].to_gym(action)
action_valid = True
observation, reward, done, info = env.step(action)
total_reward += reward
if done:
break
return -total_reward / trials
def test(self,
graph: hg.Graph,
individual,
*,
speed=1.0,
single_render_invocation=False):
env = self.env
adapters = self.adapters
fps = env.metadata['video.frames_per_second']
frame_time = 1.0 / (fps * speed)
if single_render_invocation:
env.render()
total_reward = 0
observation = env.reset()
ctx = hg.ExecutionContext(tweaks=individual)
action_prob = self.action_prob
action_valid = False
action = None
with ctx.as_default():
while True:
if not single_render_invocation:
env.render()
time.sleep(frame_time)
if (not action_valid) or action_prob == 1 or np.random.uniform(
) <= action_prob:
action = graph(input=adapters[0].from_gym(observation))
action = adapters[1].to_gym(action)
action_valid = True
observation, reward, done, info = env.step(action)
total_reward += reward
if done:
break
print('Test episode concluded, total_reward={}'.format(total_reward))
import hypergraph as hg
import numpy as np
graph1 = hg.Graph()
with graph1.as_default():
hg.mark("abc1") << (hg.dump() << "** abc1 **")
n = hg.mark("abc2") << (hg.dump() << "** abc2 **")
idx = hg.node(lambda _: np.random.randint(0, 2))
hg.output() << (hg.select(idx) << ["abc1", "abc2"])
for _ in range(3):
ctx = hg.ExecutionContext()
with ctx.as_default():
print(graph1())
print("*** end of execution ***")
import hypergraph as hg
from hypergraph.genetic import GeneticOperators
@hg.decl_tweaks(y=hg.tweaks.Uniform())
@hg.decl_tweaks(z=hg.tweaks.Normal(mean=10))
def test1(x, y, z):
return x + y + z
graph1 = hg.Graph(name='g1')
with graph1.as_default():
# Note: the dictionary items are automatically mapped to the arguments
hg.output() << (hg.call(test1) << {'x': 2})
genetic = GeneticOperators(graph1)
print(genetic.phenotype)
tweaks = genetic.create_population(1)[0]
print(tweaks)
ctx = hg.ExecutionContext(tweaks=tweaks)
with ctx.as_default():
print(graph1())