def main():
from arguments import get_args
args = get_args()
device = torch.device("cpu")
pool = Pool()
experiment_save_dir = get_experiment_save_dir(args)
if args.debug:
rollout(args,
D_IN,
D_OUT,
pool,
device,
exp_save_dir=experiment_save_dir,
pop_size=5,
elite_prop=0.2)
else:
print('there should be only one of me!')
rollout(args,
D_IN,
D_OUT,
pool,
device,
exp_save_dir=experiment_save_dir,
pop_size=args.pop_size)
def main():
args = get_args()
print("start the train function")
args.init_sigma = 0.6
args.lr = 0.001
# plot_weight_histogram(parameters)
exp_save_dir = get_experiment_save_dir(args)
inner_loop_ppo(args,
args.lr,
num_steps=1000,
num_updates=4000,
inst_on=False,
visualize=False,
save_dir=exp_save_dir)
if __name__ == "__main__":
pop_size = 504
num_steps = 1500
args = get_args()
# set up the parallelization
try:
from mpipool import Pool
pool = Pool()
except:
pool = None
experiment_save_dir = get_experiment_save_dir(args)
env_name = register_set_goal(0)
init_sigma = args.init_sigma
envs = make_vec_envs(env_name, args.seed, 1, args.gamma, None,
torch.device("cpu"), False)
if args.load_ga:
last_iter = get_start_gen_idx(args.load_ga, experiment_save_dir) - 1
start_weights = torch.load(
os.path.join(experiment_save_dir,
f"saved_weights_gen_{last_iter}.dat"))
else:
blueprint_model = init_ppo(envs, log(init_sigma))
start_weights = get_model_weights(blueprint_model)
start_weights.append(np.array([args.lr]))