def main():
env = gym.make('SlimeVolley-v0')
env.seed(123)
POPULATION_SIZE = 20
MAX_GENERATION = 100
MUTATION_RATE = 0.4
CROSSOVER_RATE = 0.7
INPUT_SIZE = 12
OUTPUT_SIZE = 3
assert POPULATION_SIZE % 2 == 0
p = Population(DeepMLPTorchIndividual(INPUT_SIZE, 0,
OUTPUT_SIZE), POPULATION_SIZE,
MAX_GENERATION, MUTATION_RATE, CROSSOVER_RATE, 0.0)
p.set_population([
DeepMLPTorchIndividual(INPUT_SIZE, 0, OUTPUT_SIZE)
for _ in range(POPULATION_SIZE)
])
p.run(
env,
generation,
verbose=True,
log=True,
output_folder=f'model-layers={INPUT_SIZE}-{HIDDEN_SIZE}-{OUTPUT_SIZE}',
save_as_pytorch=False)
if __name__ == '__main__':
env = gym.make('BipedalWalker-v2')
env.seed(123)
POPULATION_SIZE = 10
MAX_GENERATION = 10
MUTATION_RATE = 0.01
CROSSOVER_RATE = 0.8
INPUT_SIZE = 24
OUTPUT_SIZE = 4
assert POPULATION_SIZE % 2 == 0
p = Population(DeepBipedalWalkerIndividual(INPUT_SIZE, 0,
OUTPUT_SIZE), POPULATION_SIZE,
MAX_GENERATION, MUTATION_RATE, CROSSOVER_RATE, 0.0)
p.set_population([
DeepBipedalWalkerIndividual(INPUT_SIZE, 0, OUTPUT_SIZE)
for _ in range(POPULATION_SIZE)
])
p.run(
env,
generation,
verbose=True,
log=True,
output_folder=f'model-layers={INPUT_SIZE}-{HIDDEN_SIZE}-{OUTPUT_SIZE}',
save_as_pytorch=False)
env.close()
