File size: 2,681 Bytes
b86de98 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 | """MetaWorld: train policy with learned rewards."""
import os
import subprocess
from absl import app
from absl import flags
from absl import logging
from configs.constants import METAWORLDTASKS
from configs.constants import METAWORLD_TASK_TO_ENV_NAME
from torchkit.experiment import string_from_kwargs
from torchkit.experiment import unique_id
import yaml
FLAGS = flags.FLAGS
CONFIG_PATH = "configs/metaworld/rl/env_reward.py"
flags.DEFINE_enum("env_name", None, METAWORLDTASKS, "MetaWorld env to train on.")
flags.DEFINE_string("pretrained_path", None, "Path to pretraining experiment.")
flags.DEFINE_string("initial_policy_checkpoint_path", None, "Path to initial policy.")
flags.DEFINE_list("seeds", [0, 1], "List specifying the range of seeds to run.")
flags.DEFINE_string("device", "cuda:0", "The compute device.")
flags.DEFINE_integer(
"switch_to_generative_subgoals_step",
-1,
"The training step to switch to generated subgoals.",
)
def main(_):
with open(os.path.join(FLAGS.pretrained_path, "metadata.yaml"), "r") as fp:
kwargs = yaml.load(fp, Loader=yaml.FullLoader)
if kwargs["algo"] == "goal_classifier":
reward_type = "goal_classifier"
else:
reward_type = "distance_to_goal"
env_name = METAWORLD_TASK_TO_ENV_NAME[FLAGS.env_name] #METAWORLD_TASK_TO_ENV_NAME[kwargs["env_name"]]
print(f"______Training robot for {env_name}______")
# Generate a unique experiment name.
experiment_name = string_from_kwargs(
env_name=env_name,
reward="learned",
reward_type=reward_type,
algo=kwargs["algo"],
uid=unique_id(),
)
logging.info("Experiment name: %s", experiment_name)
# Execute each seed in parallel.
procs = []
for seed in range(*list(map(int, FLAGS.seeds))):
proc = subprocess.Popen([
"python",
"train_policy.py",
"--experiment_name",
experiment_name,
"--env_name",
f"{env_name}",
"--config",
f"{CONFIG_PATH}:{FLAGS.env_name}",
"--config.reward_wrapper.pretrained_path",
f"{FLAGS.pretrained_path}",
"--config.reward_wrapper.type",
f"{reward_type}",
"--seed",
f"{seed}",
"--device",
f"{FLAGS.device}",
"--initial_policy_checkpoint_path",
f"{FLAGS.initial_policy_checkpoint_path}",
"--switch_to_generative_subgoals_step",
f"{FLAGS.switch_to_generative_subgoals_step}",
])
procs.append(proc)
for p in procs:
p.wait()
if __name__ == "__main__":
app.run(main)
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