mirror of
https://github.com/vale981/ray
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fcf897ee72
update rllib example to use Tuner API. Signed-off-by: xwjiang2010 <xwjiang2010@gmail.com>
97 lines
3.1 KiB
Python
97 lines
3.1 KiB
Python
"""Example of running a custom hand-coded policy alongside trainable policies.
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This example has two policies:
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(1) a simple PG policy
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(2) a hand-coded policy that acts at random in the env (doesn't learn)
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In the console output, you can see the PG policy does much better than random:
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Result for PG_multi_cartpole_0:
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...
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policy_reward_mean:
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pg_policy: 185.23
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random: 21.255
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...
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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.tune.registry import register_env
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from ray.rllib.examples.env.multi_agent import MultiAgentCartPole
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from ray.rllib.examples.policy.random_policy import RandomPolicy
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from ray.rllib.policy.policy import PolicySpec
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from ray.rllib.utils.test_utils import check_learning_achieved
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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="tf",
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help="The DL framework specifier.",
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)
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parser.add_argument(
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"--as-test",
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action="store_true",
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help="Whether this script should be run as a test: --stop-reward must "
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"be achieved within --stop-timesteps AND --stop-iters.",
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)
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parser.add_argument(
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"--stop-iters", type=int, default=20, help="Number of iterations to train."
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)
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parser.add_argument(
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"--stop-timesteps", type=int, default=100000, help="Number of timesteps to train."
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)
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parser.add_argument(
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"--stop-reward", type=float, default=150.0, help="Reward at which we stop training."
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)
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if __name__ == "__main__":
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args = parser.parse_args()
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ray.init()
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# Simple environment with 4 independent cartpole entities
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register_env(
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"multi_agent_cartpole", lambda _: MultiAgentCartPole({"num_agents": 4})
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)
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stop = {
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"training_iteration": args.stop_iters,
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"episode_reward_mean": args.stop_reward,
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"timesteps_total": args.stop_timesteps,
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}
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config = {
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"env": "multi_agent_cartpole",
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"multiagent": {
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# The multiagent Policy map.
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"policies": {
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# The Policy we are actually learning.
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"pg_policy": PolicySpec(config={"framework": args.framework}),
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# Random policy we are playing against.
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"random": PolicySpec(policy_class=RandomPolicy),
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},
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# Map to either random behavior or PR learning behavior based on
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# the agent's ID.
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"policy_mapping_fn": (
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lambda aid, **kwargs: ["pg_policy", "random"][aid % 2]
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),
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# We wouldn't have to specify this here as the RandomPolicy does
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# not learn anyways (it has an empty `learn_on_batch` method), but
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# it's good practice to define this list here either way.
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"policies_to_train": ["pg_policy"],
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},
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"framework": args.framework,
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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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}
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results = tune.Tuner(
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"PG", param_space=config, run_config=air.RunConfig(stop=stop, verbose=1)
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).fit()
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if args.as_test:
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check_learning_achieved(results, args.stop_reward)
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ray.shutdown()
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