Official implementation of Time-Constrained Robust MDP, NeurIPS 2024.
All the environment is available in the RRLS
package. To install the package, run the following commands:
git clone https://github.com/SuReLI/RRLS
cd RRLS
pip install -e .
Then go to TCRM folder and install the requirements:
cd TCRMDP
pip install -r requirements.txt
You have multiple entry points to run the code. One per algorithm. For example, to run the Stack TC m2t3d algorithm, you can run the following command:
python main_stacked_tc_m2td3.py --help
.
└── src
├── evaluation.py # Evaluation script
├── __init__.py
├── m2td3 # This a the official implementation of M2TD3 from tanabe et al. 2022
│ ├── agent_wrapper.py
│ ├── algo.py
│ ├── factory.py
│ ├── __init__.py
│ ├── trainer.py
│ └── utils.py
├── main_dr.py # Entry point for the DR algorithm
├── main_m2td3.py # Entry point for the M2TD3 algorithm
├── main_oracle_tc_m2td3.py # Entry point for the Oracle TC M2TD3 algorithm
├── main_oracle_tc_rarl.py # Entry point for the Oracle TC RARL algorithm
├── main_rarl.py # Entry point for the RARL algorithm
├── main_stacked_tc_m2td3.py # Entry point for the Stacked TC M2TD3 algorithm
├── main_stacked_tc_rarl.py # Entry point for the Stacked TC RARL algorithm
├── main_tc_adversary.py # Entry point for train a TC adversary on a trained agent
├── main_vanilla.py # Entry point for the TD3 algorithm
├── main_vanilla_tc_m2td3.py # Entry point for the TC M2TD3 algorithm
├── main_vanilla_tc_rarl.py # Entry point for the TC RARL algorithm
├── mock_agent.py
├── scheduler.py # Fixed adversary scheduler in the paper
├── tc_mdp.py # This is where the magic happens, the implementation of the TCRMDP
├── td3
│ ├── buffer.py
│ ├── __init__.py
│ ├── models.py
│ ├── td3.py
│ └── trainer.py
└── utils.py
@inproceedings{
zouitine2024timeconstrained,
title={Time-Constrained Robust {MDP}s},
author={Adil Zouitine and David Bertoin and Pierre Clavier and Matthieu Geist and Emmanuel Rachelson},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=NKpPnb3YNg}
}