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- import argparse
- import os
- import ray
- from ray.rllib.agents.trainer_template import build_trainer
- from ray.rllib.examples.policy.bare_metal_policy_with_custom_view_reqs \
- import BareMetalPolicyWithCustomViewReqs
- from ray import tune
- def get_cli_args():
- """Create CLI parser and return parsed arguments"""
- parser = argparse.ArgumentParser()
- # general args
- parser.add_argument(
- "--run", default="PPO", help="The RLlib-registered algorithm to use.")
- parser.add_argument("--num-cpus", type=int, default=3)
- parser.add_argument(
- "--stop-iters",
- type=int,
- default=1,
- help="Number of iterations to train.")
- parser.add_argument(
- "--stop-timesteps",
- type=int,
- default=100000,
- help="Number of timesteps to train.")
- parser.add_argument(
- "--local-mode",
- action="store_true",
- help="Init Ray in local mode for easier debugging.")
- args = parser.parse_args()
- print(f"Running with following CLI args: {args}")
- return args
- if __name__ == "__main__":
- args = get_cli_args()
- ray.init(num_cpus=args.num_cpus or None, local_mode=args.local_mode)
- # Create q custom Trainer class using our custom Policy.
- BareMetalPolicyTrainer = build_trainer(
- name="MyPolicy", default_policy=BareMetalPolicyWithCustomViewReqs)
- config = {
- "env": "CartPole-v0",
- # Use GPUs iff `RLLIB_NUM_GPUS` env var set to > 0.
- "num_gpus": int(os.environ.get("RLLIB_NUM_GPUS", "0")),
- "model": {
- # Necessary to get the whole trajectory of 'state_in_0' in the
- # sample batch.
- "max_seq_len": 1,
- },
- "num_workers": 1,
- # NOTE: Does this have consequences?
- # I use it for not loading tensorflow/pytorch.
- "framework": None,
- "log_level": "DEBUG",
- "create_env_on_driver": True,
- }
- stop = {
- "training_iteration": args.stop_iters,
- "timesteps_total": args.stop_timesteps,
- }
- # Train the Trainer with our policy.
- results = tune.run(BareMetalPolicyTrainer, config=config, stop=stop)
- print(results)
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