Skip to content
forked from yardenas/la-mbda

LAMBDA is a model-based reinforcement learning agent that uses Bayesian world models for safe policy optimization

License

Notifications You must be signed in to change notification settings

Harry-mic/la-mbda

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

10 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

LAMBDA

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}
}

Idea

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.

Running and plotting

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

Acknowledgement

Dreamer codebase which served as a starting point for this github repo

About

LAMBDA is a model-based reinforcement learning agent that uses Bayesian world models for safe policy optimization

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 100.0%