def eval_genome(genome, config):
net = neat.nn.FeedForwardNetwork.create(genome, config)
fitnesses = []
for runs in range(runs_per_net):
sim = Simulator(testNum=runs)
# Run the given simulation for up to num_steps time steps.
while sim.shouldContinue():
inputs = sim.getInputActivations()
action = net.activate(inputs)
# Apply action to the simulated cart-pole
sim.update(action[0], action[1])
fitnesses.append(sim.getFitness())
# The genome's fitness is its worst performance across all runs.
return statistics.mean(fitnesses)
