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>
119 lines
3.7 KiB
Python
119 lines
3.7 KiB
Python
"""Simple example of setting up a multi-agent policy mapping.
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Control the number of agents and policies via --num-agents and --num-policies.
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This works with hundreds of agents and policies, but note that initializing
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many TF policies will take some time.
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Also, TF evals might slow down with large numbers of policies. To debug TF
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execution, set the TF_TIMELINE_DIR environment variable.
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"""
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import argparse
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import os
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import random
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import ray
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from ray import air, tune
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from ray.rllib.examples.env.multi_agent import MultiAgentCartPole
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from ray.rllib.examples.models.shared_weights_model import (
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SharedWeightsModel1,
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SharedWeightsModel2,
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TF2SharedWeightsModel,
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TorchSharedWeightsModel,
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)
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from ray.rllib.models import ModelCatalog
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from ray.rllib.policy.policy import PolicySpec
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from ray.rllib.utils.framework import try_import_tf
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from ray.rllib.utils.test_utils import check_learning_achieved
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tf1, tf, tfv = try_import_tf()
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parser = argparse.ArgumentParser()
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parser.add_argument("--num-agents", type=int, default=4)
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parser.add_argument("--num-policies", type=int, default=2)
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parser.add_argument("--num-cpus", type=int, default=0)
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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=200, 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(num_cpus=args.num_cpus or None)
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# Register the models to use.
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if args.framework == "torch":
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mod1 = mod2 = TorchSharedWeightsModel
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elif args.framework in ["tfe", "tf2"]:
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mod1 = mod2 = TF2SharedWeightsModel
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else:
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mod1 = SharedWeightsModel1
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mod2 = SharedWeightsModel2
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ModelCatalog.register_custom_model("model1", mod1)
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ModelCatalog.register_custom_model("model2", mod2)
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# Each policy can have a different configuration (including custom model).
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def gen_policy(i):
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config = {
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"model": {
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"custom_model": ["model1", "model2"][i % 2],
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},
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"gamma": random.choice([0.95, 0.99]),
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}
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return PolicySpec(config=config)
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# Setup PPO with an ensemble of `num_policies` different policies.
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policies = {"policy_{}".format(i): gen_policy(i) for i in range(args.num_policies)}
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policy_ids = list(policies.keys())
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def policy_mapping_fn(agent_id, episode, worker, **kwargs):
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pol_id = random.choice(policy_ids)
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return pol_id
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config = {
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"env": MultiAgentCartPole,
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"env_config": {
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"num_agents": args.num_agents,
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},
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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_sgd_iter": 10,
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"multiagent": {
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"policies": policies,
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"policy_mapping_fn": policy_mapping_fn,
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},
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"framework": args.framework,
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}
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stop = {
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"episode_reward_mean": args.stop_reward,
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"timesteps_total": args.stop_timesteps,
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"training_iteration": args.stop_iters,
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}
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results = tune.Tuner(
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"PPO", 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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