A repository for the implementation of the paper Constrained Policy Optimization via Bayesian World Models (Yarden As, Ilnura Usmanova, Sebastian Curi, Andreas Krause, ICLR 2022). Please see our paper (arXiv) for further details. To cite, please use:
@article{as2022constrained,
title={Constrained Policy Optimization via Bayesian World Models},
author={As, Yarden and Usmanova, Ilnura and Curi, Sebastian and Krause, Andreas},
journal={arXiv preprint arXiv:2201.09802},
year={2022}
}
By taking a Bayesian perspective, LAMBDA learns a world model and uses it to generate sequences using different posterior samples of the world model parameters. Following that, it chooses and learns from the optimistic sequences how to solve the task and from the pessimistic sequences how to adhere to safety restrictions.
Install dependencies (this may take more than an hour):
conda create -n lambda python=3.6
conda activate lambda
pip3 install .
Run experiments:
python3 experiments/train.py --log_dir results/la_mbda/point_goal2/314 --environment sgym_Safexp-PointGoal2-v0 --total_training_steps 1000000 --safety
Plot:
python3 experiments/plot.py --data_path results/
where the script expects the following directory tree structure:
results
├── algo1
│ └── environment1
│ └── experiment1
│ └── ...
│ └── ...
└── algo2
└── environment1
├── experiment1
└── experiment2
Dreamer codebase which served as a starting point for this github repo
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent