81 lines
2.5 KiB
Python
81 lines
2.5 KiB
Python
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import os.path
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from metadrive.envs.scenario_env import ScenarioEnv
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from scenarionet import SCENARIONET_REPO_PATH, SCENARIONET_DATASET_PATH
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from scenarionet_training.train_utils.multi_worker_PPO import MultiWorkerPPO
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from scenarionet_training.train_utils.utils import train, get_train_parser, get_exp_name
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if __name__ == '__main__':
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env = ScenarioEnv
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args = get_train_parser().parse_args()
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exp_name = get_exp_name(args)
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stop = int(100_000_000)
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config = dict(
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env=env,
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env_config=dict(
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# scenario
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start_scenario_index=0,
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num_scenarios=32,
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data_directory=os.path.join(SCENARIONET_DATASET_PATH, "pg"),
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sequential_seed=True,
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# traffic & light
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reactive_traffic=False,
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no_static_vehicles=True,
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no_light=True,
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static_traffic_object=True,
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# curriculum training
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curriculum_level=4,
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target_success_rate=0.8,
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# training
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horizon=None,
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use_lateral_reward=True,
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),
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# # ===== Evaluation =====
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evaluation_interval=2,
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evaluation_num_episodes=32,
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evaluation_config=dict(env_config=dict(start_scenario_index=32,
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num_scenarios=32,
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sequential_seed=True,
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curriculum_level=1, # turn off
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data_directory=os.path.join(SCENARIONET_DATASET_PATH, "pg"))),
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evaluation_num_workers=2,
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metrics_smoothing_episodes=10,
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# ===== Training =====
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model=dict(fcnet_hiddens=[512, 256, 128]),
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horizon=600,
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num_sgd_iter=20,
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lr=5e-5,
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rollout_fragment_length=500,
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sgd_minibatch_size=100,
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train_batch_size=4000,
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num_gpus=0.5 if args.num_gpus != 0 else 0,
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num_cpus_per_worker=0.4,
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num_cpus_for_driver=1,
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num_workers=2,
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framework="tf"
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)
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train(
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MultiWorkerPPO,
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exp_name=exp_name,
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save_dir=os.path.join(SCENARIONET_REPO_PATH, "experiment"),
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keep_checkpoints_num=5,
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stop=stop,
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config=config,
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num_gpus=args.num_gpus,
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# num_seeds=args.num_seeds,
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num_seeds=1,
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# test_mode=args.test,
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# local_mode=True,
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# TODO remove this when we release our code
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# wandb_key_file="~/wandb_api_key_file.txt",
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wandb_project="scenarionet",
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)
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