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- """
- Example for using _disable_preprocessor_api=True to disable all preprocessing.
- This example shows:
- - How a complex observation space from the env is handled directly by the
- model.
- - Complex observations are flattened into lists of tensors and as such
- stored by the SampleCollectors.
- - This has the advantage that preprocessing happens in batched fashion
- (in the model).
- """
- import argparse
- from gym.spaces import Box, Dict, Discrete, MultiDiscrete, Tuple
- import numpy as np
- import os
- import ray
- 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(
- "--framework",
- choices=["tf", "tf2", "tfe", "torch"],
- default="tf",
- help="The DL framework specifier.")
- parser.add_argument(
- "--stop-iters",
- type=int,
- default=200,
- help="Number of iterations to train.")
- parser.add_argument(
- "--stop-timesteps",
- type=int,
- default=500000,
- help="Number of timesteps to train.")
- parser.add_argument(
- "--stop-reward",
- type=float,
- default=80.0,
- help="Reward at which we stop training.")
- parser.add_argument(
- "--as-test",
- action="store_true",
- help="Whether this script should be run as a test: --stop-reward must "
- "be achieved within --stop-timesteps AND --stop-iters.")
- parser.add_argument(
- "--no-tune",
- action="store_true",
- help="Run without Tune using a manual train loop instead. Here,"
- "there is no TensorBoard support.")
- 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(local_mode=args.local_mode)
- config = {
- "env": "ray.rllib.examples.env.random_env.RandomEnv",
- "env_config": {
- "config": {
- "observation_space": Dict({
- "a": Discrete(2),
- "b": Dict({
- "ba": Discrete(3),
- "bb": Box(-1.0, 1.0, (2, 3), dtype=np.float32)
- }),
- "c": Tuple((MultiDiscrete([2, 3]), Discrete(2))),
- "d": Box(-1.0, 1.0, (2, ), dtype=np.int32),
- }),
- },
- },
- # Set this to True to enforce no preprocessors being used.
- # Complex observations now arrive directly in the model as
- # structures of batches, e.g. {"a": tensor, "b": [tensor, tensor]}
- # for obs-space=Dict(a=..., b=Tuple(..., ...)).
- "_disable_preprocessor_api": True,
- # Use GPUs iff `RLLIB_NUM_GPUS` env var set to > 0.
- "num_gpus": int(os.environ.get("RLLIB_NUM_GPUS", 0)),
- "framework": args.framework,
- }
- stop = {
- "training_iteration": args.stop_iters,
- "timesteps_total": args.stop_timesteps,
- "episode_reward_mean": args.stop_reward,
- }
- results = tune.run(args.run, config=config, stop=stop, verbose=2)
- ray.shutdown()
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