This code base reproduces the experiments of the ICLR 2022 paper "Modular Lifelong Reinforcement Learning via Neural Composition".

Primary dependencies:

• Python 3.6
• gym-minigrid: install local version via pip install -e gym-minigrid
• torch-ac-composable: install local version via pip install -e torch-ac-composable
• pytorch version 1.5.1

To reproduce the results of our compositional agent, execute the following command from the torch-ac-composable/torch_ac_composable/ directory:

python -m experiments.ppo_minigrid_lifelong --algo comp-ppo --learning-rate 1e-3 --steps-per-proc 256 --batch-size 64 --procs 16 --num-tasks 64 --num-steps 1000000 --max-modules 4

Primary dependencies:

• Python 3.6
• Spinning Up: install local version via pip install -e spinningup
• Robosuite: install local version via pip install -e robosuite
• pytorch version 1.8.1

To reproduce the results of our compositional agent, execute the following command from the training/ directory:

python train_lifelong_ppo.py --algo comp-ppo --num-tasks 48 --cpu 40 --gamma 0.995 --epochs 150 --steps 8000

If you use this work, please make sure to cite our paper:

@inproceedings{
    mendez2022modular,
    title={Modular Lifelong Reinforcement Learning via Neural Composition},
    author={Jorge A Mendez and Harm van Seijen and Eric Eaton},
    booktitle={International Conference on Learning Representations},
    year={2022},
    url={https://openreview.net/forum?id=5XmLzdslFNN}
}