if __name__ == "__main__":
# Parse command line arguments
args = get_argparser().parse_args()
# Experiment (set seed before creating the modules)
ex_dir = setup_experiment(
QQubeSwingUpSim.name,
f"{SimOpt.name}-{NES.name}-{PPO.name}_{FNNPolicy.name}")
num_workers = 16
# Set seed if desired
pyrado.set_seed(args.seed, verbose=True)
# Environments
env_hparams = dict(dt=1 / 100.0, max_steps=600)
env_real = QQubeSwingUpReal(**env_hparams)
env_sim = QQubeSwingUpSim(**env_hparams)
randomizer = DomainRandomizer(
NormalDomainParam(name="mass_rot_pole",
mean=0.0,
std=1e6,
clip_lo=1e-3),
NormalDomainParam(name="mass_pend_pole",
mean=0.0,
std=1e6,
clip_lo=1e-3),
NormalDomainParam(name="length_rot_pole",
mean=0.0,
std=1e6,
clip_lo=1e-3),
def default_qq_real():
return QQubeSwingUpReal(dt=1 / 500.0, max_steps=500)
"c")
elif args.env_name == QCartPoleStabReal.name:
env = QCartPoleStabReal(args.dt, args.max_steps)
policy = QCartPoleSwingUpAndBalanceCtrl(env.spec)
print_cbt("Set up controller for the QCartPoleStabReal environment.",
"c")
elif args.env_name == QCartPoleSwingUpReal.name:
env = QCartPoleSwingUpReal(args.dt, args.max_steps)
policy = QCartPoleSwingUpAndBalanceCtrl(env.spec)
print_cbt(
"Set up controller for the QCartPoleSwingUpReal environment.", "c")
elif args.env_name == QQubeSwingUpReal.name:
env = QQubeSwingUpReal(args.dt, args.max_steps)
policy = QQubeSwingUpAndBalanceCtrl(env.spec)
print_cbt("Set up controller for the QQubeSwingUpReal environment.",
"c")
else:
raise pyrado.ValueErr(
given=args.env_name,
eq_constraint=
f"{QBallBalancerReal.name}, {QCartPoleSwingUpReal.name}, "
f"{QCartPoleStabReal.name}, or {QQubeSwingUpReal.name}",
)
# Run on device
done = False
print_cbt("Running predefined controller ...", "c", bright=True)
policy.reset(**dict(domain_param=ml_domain_param))
elif args.src_domain_param == "posterior":
prefix_str = "" if args.iter == -1 and args.round == -1 else f"iter_{args.iter}_round_{args.round}"
posterior = pyrado.load("posterior.pt", ex_dir, prefix=prefix_str)
elif args.src_domain_param == "prior":
prior = pyrado.load("prior.pt", ex_dir)
elif args.src_domain_param == "nominal":
policy.reset(**dict(domain_param=env_sim.get_nominal_domain_param()))
# Detect the correct real-world counterpart and create it
if isinstance(inner_env(env_sim), QBallBalancerSim):
env_real = QBallBalancerReal(dt=args.dt, max_steps=args.max_steps)
elif isinstance(inner_env(env_sim), QCartPoleSim):
env_real = QCartPoleReal(dt=args.dt, max_steps=args.max_steps)
elif isinstance(inner_env(env_sim), QQubeSim):
env_real = QQubeSwingUpReal(dt=args.dt, max_steps=args.max_steps)
else:
raise pyrado.TypeErr(
given=env_sim,
expected_type=[QBallBalancerSim, QCartPoleSim, QQubeSim])
# Wrap the real environment in the same way as done during training
env_real = wrap_like_other_env(env_real, env_sim)
# Run on device
done, first_round = False, True
print_cbt("Running loaded policy ...", "c", bright=True)
while not done:
# sample new domain parameter
if (args.resample or first_round) and args.src_domain_param in [
"posterior", "prior"
# Get the experiment's directory to load from if not given as command line argument
ex_dir = ask_for_experiment(
hparam_list=args.show_hparams) if args.dir is None else args.dir
# Load the policy and the environment (for constructing the real-world counterpart)
env_sim, policy, _ = load_experiment(ex_dir, args)
if "argmax" in args.policy_name:
policy = to.load(osp.join(ex_dir, "policy_argmax.pt"))
print_cbt(f"Loaded {osp.join(ex_dir, 'policy_argmax.pt')}",
"g",
bright=True)
# Create real-world counterpart
max_steps = args.max_steps if args.max_steps < pyrado.inf else env_sim.max_steps
dt = args.dt if args.dt is not None else env_sim.dt
env_real = QQubeSwingUpReal(dt, max_steps)
print_cbt(
f"Set up the QQubeSwingUpReal environment with dt={env_real.dt} max_steps={env_real.max_steps}.",
"c")
# Finally wrap the env in the same as done during training
env_real = wrap_like_other_env(env_real, env_sim)
ex_ts = datetime.now().strftime(pyrado.timestamp_format)
save_dir = osp.join(ex_dir, "evaluation")
os.makedirs(save_dir, exist_ok=True)
num_rollouts_per_config = args.num_rollouts_per_config if args.num_rollouts_per_config is not None else 5
est_ret = BayRn.eval_policy(save_dir,
env_real,
policy,
mc_estimator=True,