Training with RLLib

We provide a script demonstrating how to use RLLib to train RL agents:

# Make sure current folder does not have a sub-folder named metadrive
python -m metadrive.examples.train_generalization_experiment

# You can also use GPUs and customized experiment name:
python -m metadrive.examples.train_generalization_experiment \

In this example, we leave the training hyper-parameter config["num_envs_per_worker"] = 1 as default, so that each process (ray worker) will only contain one MetaDrive instance. We further set the evaluation workers config["evaluation_num_workers"] = 5, so that the test set environments are hosted in separated processes. By utilizing the feature of RLLib, we avoid the issue of multiple MetaDrive instances in single process.

We welcome more examples using MetaDrive in different context! Please show off your code if you like to share it by opening new issue! Thanks!


We tested this script using ray==1.2.0. If you find this script not compatible with newer RLLib, please contact us.