This repository contains code for the paper "Model-based Safe Deep Reinforcement Learning via a Constrained Proximal Policy Optimization Algorithm" accepted at NeurIPS 2022. Read paper here.
1) Requirements -
a) Python 3.7+
b) PyTorch==1.10.0 and cuda11.3
c) numpy==1.21.4
d) gym==0.15.7
e) Hardware : Cuda supported GPU with atleast 4GB memory
2) Install mujoco200 using https://roboti.us/download/mujoco200_linux.zip
3) Install Safety Gym using https://github.com/openai/safety-gym
4) For reproducing results (upto same extent because of seed randomness) -
a) Take backup of /…/safety-gym/safety_gym/envs/suite.py
b) Copy ./src/env_suite_file/suite.py to above path. This removes "Vases" and increases "Hazards" from 10 to 15.
c) Change ‘num_steps’ = 750’ in ‘DEFAULT’ dict of class Engine in /…/safety-gym/safety_gym/envs/engine.py
d) Run for 8 random seeds :
i) cd src
ii) python3 mbppo_lagrangian.py –exp_name=”experiment_name” –seed=0 –env=”<environment_name>” –beta=0.02
Where environment names are [Safexp-PointGoal2-v0,Safexp-CarGoal2-v0]
5) Use https://github.com/openai/safety-starter-agents/blob/master/scripts/plot.py for plotting -
a) python plot.py –logdir=’<path to data>’’ --value=<plot_choice>
Where plot_choice are ‘AverageEpRet’ for reward performance, ‘AverageEpCost’ for cost performance.
If you are using this in your work, please cite using :
@inproceedings{NEURIPS2022_9a8eb202, author = {Jayant, Ashish K and Bhatnagar, Shalabh}, booktitle = {Advances in Neural Information Processing Systems}, editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh}, pages = {24432--24445}, publisher = {Curran Associates, Inc.}, title = {Model-based Safe Deep Reinforcement Learning via a Constrained Proximal Policy Optimization Algorithm}, url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/9a8eb202c060b7d81f5889631cbcd47e-Paper-Conference.pdf}, volume = {35}, year = {2022} }
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent