Learning to Stabilize Online Reinforcement Learning in Unbounded State Spaces (ICML 2024)

This is the source code accompanying the paper Learning to Stabilize Online Reinforcement Learning in Unbounded State Spaces by Brahma S. Pavse, Matthew Zurek, Yudong Chen, Qiaomin Xie, Josiah P. Hanna.

Setting up the environment

conda env create -f environment.yml

Running the code

Generic command:

python run_single_continual.py  --outfile <result_file> --env_name <queue/nmodel> --mdp_num <0/1/2> --deployed_interaction_steps 5_000_000  --exp_name <exp_name>  --reward_function <opt/stab>  --seed 0  --truncated_horizon 200 --algo_name <algo_name> --lr 3e-4 --state_transformation <state_trans> --lyp_power <p> --adam_beta 0.9

where,

• exp_name can be anything
• reward_function is either opt for optimal only or stab for optimal + stability
• algo_name is either MW, PPO, or STOP-suffix where suffix can be anything to uniquely identify the algorithm run based state transformation and lyp power. Example: STOP-SL-2, denotes STOP with symloge and p = 2
• state_transformation is either id, sigmoid, symsqrt, symloge
• lyp_power is any floating number (p from the paper)

Example command:

python run_single_continual.py  --outfile result_file --env_name queue --mdp_num 2 --deployed_interaction_steps 5_000_000  --exp_name test  --reward_function stab  --seed 0  --truncated_horizon 200 --algo_name STOP-3 --lr 3e-4 --state_transformation sigmoid --lyp_power 3 --adam_beta 0.9

Citation

If you found any part of this code useful, please consider citing our paper:

@inproceedings{
    pavse2024unbounded,
    title={Learning to Stabilize Online Reinforcement Learning in Unbounded State Spaces},
    author={Brahma S. Pavse, Matthew Zurek, Yudong Chen, Qiaomin Xie, Josiah P. Hanna},
    booktitle={Forty-first International Conference on Machine Learning},
    year={2024},
    url={https://openreview.net/forum?id=64fdhmogiD}
}

Contact

If you have any questions, please feel free to email: [email protected]!