All requirements are specified in requirements.txt.
We also provide a yaml file: omrl.yml. Run: conda env create -f omrl.yml and activate the env with conda activate omrl.
For the MuJoCo-based experiments (Half-Cheetah-Vel and Ant-Semi-circle you will need a MuJoCo license).

The main script for data collecion is train_single_agent.py.
Configuration files are in data_collection_config. All training parameters can be set from within the files, or by passing command line arguments.

Run:
python train_single_agent.py --env-type X --seed Y

where X is a domain (e.g., gridworld, point_robot_sparse, cheetah_vel, ant_semicircle_sparse, point_robot_wind, escape_room) and Y is some integer (e.g. 73).
This will train a standard RL agent (implemented in learner.py) to solve a single task. Different seeds correspond to different tasks (from the same task distribution).

The main script for the VAE training is train_vae_offline.py.
Configuration files are in vae_config. All training parameters can be set from within the files, or by passing command line arguments.

Run (for example):
python train_vae_offline.py --env-type ant_semicircle_sparse

This will train the VAE (implemented in models\vae.py).

Reward Relabelling (RR) is used when the argument --hindsight-relabelling is set to True.

The main script for the offline meta-RL training is train_agent_offline.py.
Configuration files are in offline_config. All training parameters can be set from within the files, or by passing command line arguments.

Run (for example):
python train_offline_agent.py --env-type ant_semicircle_sparse

Note the --transform-data-bamdp argument. When training the meta-RL agent for the first time, this argument should be set to True in order to perform State Relabelling. That is, after loading the datasets and the trained vae, the datasets will be passed through the encoder to produce the approximate belief. This belief is then concatenated to the states in our data to form the hyper-states on which our meta-RL agent is trained. This new dataset (with hyper-states) is also saved locally. If this dataset is available (e.g., after already running the script), you can change the argument to False in order to save time.

For the online training, run: python online_training.py --env-type X where X is a domain (see Data Collection part above). Configuration files are in online_config. All training parameters can be set from within the files, or by passing command line arguments.

@inproceedings{dorfman2021offline,
  title={Offline Meta Reinforcement Learning--Identifiability Challenges and Effective Data Collection Strategies},
  author={Dorfman, Ron and Shenfeld, Idan and Tamar, Aviv},
  booktitle={Thirty-Fifth Conference on Neural Information Processing Systems},
  year={2021}
}

For any questions, please contact Ron Dorfman: [email protected]