def eval_genome(genome, config):
net = neat.nn.FeedForwardNetwork.create(genome, config)
fitnesses = []
for runs in range(runs_per_net):
game = Game(20, 20)
# Run the given simulation for up to num_steps time steps.
fitness = 0.0
while True:
inputs = game.get_normalized_state()
action = net.activate(inputs)
# Apply action to the simulated snake
valid = game.step(np.argmax(action))
# Stop if the network fails to keep the snake within the boundaries or hits itself.
# The per-run fitness is the number of pills eaten
if not valid:
break
fitness = game.fitness
fitnesses.append(fitness)
# The genome's fitness is its worst performance across all runs.
return min(fitnesses)
def eval_genome(genome, config):
net = neat.nn.FeedForwardNetwork.create(genome, config)
fitnesses = []
for runs in range(runs_per_net):
#pygame.init()
#screen = pygame.display.set_mode((20 * 16,20 * 16))
#screen.fill(pygame.Color('black'))
#pygame.display.set_caption('Snake')
#pygame.display.flip()
sim = Game(20, 20)
# Run the given simulation for up to num_steps time steps.
fitness = 0.0
while True:
inputs = sim.get_normalized_state()
action = net.activate(inputs)
# Apply action to the simulated snake
valid = sim.step(np.argmax(action))
# Stop if the network fails to keep the snake within the boundaries or hits itself.
# The per-run fitness is the number of pills eaten
if not valid:
break
fitness = sim.score
fitnesses.append(fitness)
# The genome's fitness is its worst performance across all runs.
return min(fitnesses)
