wehub-resource-sync
50 lines
1.5 KiB
Python
50 lines
1.5 KiB
Python
from ray.rllib.algorithms.appo import APPOConfig
|
|
from ray.rllib.core.rl_module.default_model_config import DefaultModelConfig
|
|
from ray.rllib.examples.envs.classes.multi_agent import MultiAgentCartPole
|
|
from ray.rllib.examples.utils import (
|
|
add_rllib_example_script_args,
|
|
run_rllib_example_script_experiment,
|
|
)
|
|
from ray.rllib.utils.metrics import (
|
|
ENV_RUNNER_RESULTS,
|
|
EPISODE_RETURN_MEAN,
|
|
NUM_ENV_STEPS_SAMPLED_LIFETIME,
|
|
)
|
|
from ray.tune.registry import register_env
|
|
|
|
parser = add_rllib_example_script_args(default_timesteps=2000000)
|
|
parser.set_defaults(
|
|
num_agents=2,
|
|
)
|
|
# Use `parser` to add your own custom command line options to this script
|
|
# and (if needed) use their values to set up `config` below.
|
|
args = parser.parse_args()
|
|
|
|
register_env("env", lambda cfg: MultiAgentCartPole(config=cfg))
|
|
|
|
|
|
config = (
|
|
APPOConfig()
|
|
.environment("env", env_config={"num_agents": args.num_agents})
|
|
.training(
|
|
vf_loss_coeff=0.005,
|
|
entropy_coeff=0.0,
|
|
)
|
|
.rl_module(
|
|
model_config=DefaultModelConfig(vf_share_layers=True),
|
|
)
|
|
.multi_agent(
|
|
policy_mapping_fn=(lambda agent_id, episode, **kwargs: f"p{agent_id}"),
|
|
policies={f"p{i}" for i in range(args.num_agents)},
|
|
)
|
|
)
|
|
|
|
stop = {
|
|
f"{ENV_RUNNER_RESULTS}/{EPISODE_RETURN_MEAN}": 350.0 * args.num_agents,
|
|
f"{NUM_ENV_STEPS_SAMPLED_LIFETIME}": args.stop_timesteps,
|
|
}
|
|
|
|
|
|
if __name__ == "__main__":
|
|
run_rllib_example_script_experiment(config, args, stop=stop)
|