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* Fix QMix, SAC, and MADDPA too. * Unpin gym and deprecate pendulum v0 Many tests in rllib depended on pendulum v0, however in gym 0.21, pendulum v0 was deprecated in favor of pendulum v1. This may change reward thresholds, so will have to potentially rerun all of the pendulum v1 benchmarks, or use another environment in favor. The same applies to frozen lake v0 and frozen lake v1 Lastly, all of the RLlib tests and have been moved to python 3.7 * Add gym installation based on python version. Pin python<= 3.6 to gym 0.19 due to install issues with atari roms in gym 0.20 * Reformatting * Fixing tests * Move atari-py install conditional to req.txt * migrate to new ale install method * Fix QMix, SAC, and MADDPA too. * Unpin gym and deprecate pendulum v0 Many tests in rllib depended on pendulum v0, however in gym 0.21, pendulum v0 was deprecated in favor of pendulum v1. This may change reward thresholds, so will have to potentially rerun all of the pendulum v1 benchmarks, or use another environment in favor. The same applies to frozen lake v0 and frozen lake v1 Lastly, all of the RLlib tests and have been moved to python 3.7 * Add gym installation based on python version. Pin python<= 3.6 to gym 0.19 due to install issues with atari roms in gym 0.20 Move atari-py install conditional to req.txt migrate to new ale install method Make parametric_actions_cartpole return float32 actions/obs Adding type conversions if obs/actions don't match space Add utils to make elements match gym space dtypes Co-authored-by: Jun Gong <jungong@anyscale.com> Co-authored-by: sven1977 <svenmika1977@gmail.com>
72 lines
2.1 KiB
Python
72 lines
2.1 KiB
Python
import argparse
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import os
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import ray
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from ray.rllib.agents.trainer_template import build_trainer
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from ray.rllib.examples.policy.bare_metal_policy_with_custom_view_reqs \
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import BareMetalPolicyWithCustomViewReqs
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from ray import tune
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def get_cli_args():
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"""Create CLI parser and return parsed arguments"""
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parser = argparse.ArgumentParser()
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# general args
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parser.add_argument(
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"--run", default="PPO", help="The RLlib-registered algorithm to use.")
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parser.add_argument("--num-cpus", type=int, default=3)
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parser.add_argument(
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"--stop-iters",
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type=int,
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default=1,
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help="Number of iterations to train.")
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parser.add_argument(
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"--stop-timesteps",
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type=int,
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default=100000,
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help="Number of timesteps to train.")
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parser.add_argument(
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"--local-mode",
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action="store_true",
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help="Init Ray in local mode for easier debugging.")
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args = parser.parse_args()
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print(f"Running with following CLI args: {args}")
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return args
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if __name__ == "__main__":
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args = get_cli_args()
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ray.init(num_cpus=args.num_cpus or None, local_mode=args.local_mode)
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# Create q custom Trainer class using our custom Policy.
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BareMetalPolicyTrainer = build_trainer(
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name="MyPolicy", default_policy=BareMetalPolicyWithCustomViewReqs)
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config = {
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"env": "CartPole-v0",
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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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"model": {
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# Necessary to get the whole trajectory of 'state_in_0' in the
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# sample batch.
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"max_seq_len": 1,
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},
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"num_workers": 1,
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# NOTE: Does this have consequences?
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# I use it for not loading tensorflow/pytorch.
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"framework": None,
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"log_level": "DEBUG",
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"create_env_on_driver": True,
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}
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stop = {
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"training_iteration": args.stop_iters,
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"timesteps_total": args.stop_timesteps,
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}
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# Train the Trainer with our policy.
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results = tune.run(BareMetalPolicyTrainer, config=config, stop=stop)
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print(results)
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