mirror of
https://github.com/vale981/ray
synced 2025-03-04 17:41:43 -05:00
fcf897ee72
update rllib example to use Tuner API. Signed-off-by: xwjiang2010 <xwjiang2010@gmail.com>
58 lines
1.6 KiB
Python
58 lines
1.6 KiB
Python
"""Example of using variable-length Repeated / struct observation spaces.
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This example shows:
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- using a custom environment with Repeated / struct observations
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- using a custom model to view the batched list observations
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For PyTorch / TF eager mode, use the `--framework=[torch|tf2|tfe]` flag.
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"""
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import argparse
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import os
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import ray
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from ray import air, tune
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from ray.rllib.models import ModelCatalog
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from ray.rllib.examples.env.simple_rpg import SimpleRPG
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from ray.rllib.examples.models.simple_rpg_model import (
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CustomTorchRPGModel,
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CustomTFRPGModel,
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)
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--framework",
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choices=["tf", "tf2", "tfe", "torch"],
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default="tf2",
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help="The DL framework specifier.",
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)
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if __name__ == "__main__":
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ray.init()
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args = parser.parse_args()
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if args.framework == "torch":
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ModelCatalog.register_custom_model("my_model", CustomTorchRPGModel)
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else:
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ModelCatalog.register_custom_model("my_model", CustomTFRPGModel)
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config = {
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"framework": args.framework,
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"env": SimpleRPG,
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"rollout_fragment_length": 1,
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"train_batch_size": 2,
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# Use GPUs iff `RLLIB_NUM_GPUS` env var set to > 0.
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"num_gpus": int(os.environ.get("RLLIB_NUM_GPUS", "0")),
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"num_workers": 0,
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"model": {
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"custom_model": "my_model",
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},
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"_disable_preprocessor_api": False,
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}
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stop = {
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"timesteps_total": 1,
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}
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tuner = tune.Tuner(
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"PG", param_space=config, run_config=air.RunConfig(stop=stop, verbose=1)
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)
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