This is the repository for our work, Cautious Adaptation for RL in Safety-Critical Settings (CARL).
Our code is based off of the repository at https://github.com/quanvuong/handful-of-trials-pytorch, which is a Pytorch version of the original PETS paper repository located here: PETS-Tensorflow.
Clone this repository with git clone --recursive https://github.com/jesbu1/carl.git.
This recursively clone the two submodules required, gym-duckietown (code for the modified Duckietown driving environment) and learning_to_adapt (code containing the Half-Cheetah Disabled Joint environment from GrBAL .
In order to experiment on MuJoco environments, you must have MuJoco 200 installed with an appropriate MuJuco license linked.
See here to download and setup MuJoco 200: mujoco. On Ubuntu, we had to install some extra packages first: sudo apt install -y libosmesa6-dev libgl1-mesa-glx libglfw3 patchelf.
To install the base packages required, simply pip install -r requirements.txt (Tested with Python 3.6).
Then you must pip install -e gym-duckietown to install the modified Duckietown driving environment, and pip install -e learning_to_adapt
to install the disabled joint Half-Cheetah environment code originally from GrBAL.
For PDDM Baoding Hand manipulation experiments, see this repo here.
Experiments for the other three environments can be run using:
python mbexp.py
--env ENV (required) The name of the environment. Select from
[cartpole, halfcheetahdisabled, duckietown].
Results will be saved in <logdir>/<date+time of experiment start>/.
Trial data will be contained in <logdir>/<date+time of experiment start>-tboard,
You can run tensorboard --logdir <logdir> to visualize the results
To run CARL with catastrophic state risk aversion, you don't need any extra flags.
To run CARL (Reward), you can run:
python mbexp.py --env ENV --no_catastrophe_pred --percentile PERCENTILE --test_percentile TEST_PERCENTILE
This turns off catastrophe prediction. We train with percentile (gamma) = 100 and test_percentile (gamma) = 50 for all environments.
To run MB + Finetune, you can run:
python mbexp.py --env ENV --no_catastrophe_pred --test_percentile 100
Configuration files are located in config/, modify these python files to change some environment/model/training parameters.
The ensemble model class is also located in config/.
env/ contains gym environment files.
MPC.py contains training code and acting code for the MPC controller.
optimizers.py contains the optimizers (CEM) used for optimizing actions with MPC.
MBExperiment.py contains training, adaptation, and testing loop code.
Agent.py contains logic for interacting with the environment with model-based planning and collecting samples
In order to run the Duckietown car-driving environment, you need to have either a real or virtual display.
This is an unfortunate restriction of the original environment. One option is to create a Docker container,
another is install xvfb and running xvfb-run python mbexp.py ...., however we have had limited luck in
getting this to work on displayless servers.
You can cite CARL with this example bibtex
@InProceedings{pmlr-v119-zhang20e,
title = {Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings},
author = {Zhang, Jesse and Cheung, Brian and Finn, Chelsea and Levine, Sergey and Jayaraman, Dinesh},
booktitle = {Proceedings of the 37th International Conference on Machine Learning},
pages = {11055--11065},
year = {2020},
editor = {III, Hal Daumé and Singh, Aarti},
volume = {119},
series = {Proceedings of Machine Learning Research},
month = {13--18 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v119/zhang20e/zhang20e.pdf},
url = {https://proceedings.mlr.press/v119/zhang20e.html},
abstract = {Reinforcement learning (RL) in real-world safety-critical target settings like urban driving is hazardous, imperiling the RL agent, other agents, and the environment. To overcome this difficulty, we propose a "safety-critical adaptation" task setting: an agent first trains in non-safety-critical "source" environments such as in a simulator, before it adapts to the target environment where failures carry heavy costs. We propose a solution approach, CARL, that builds on the intuition that prior experience in diverse environments equips an agent to estimate risk, which in turn enables relative safety through risk-averse, cautious adaptation. CARL first employs model-based RL to train a probabilistic model to capture uncertainty about transition dynamics and catastrophic states across varied source environments. Then, when exploring a new safety-critical environment with unknown dynamics, the CARL agent plans to avoid actions that could lead to catastrophic states. In experiments on car driving, cartpole balancing, and half-cheetah locomotion, CARL successfully acquires cautious exploration behaviors, yielding higher rewards with fewer failures than strong RL adaptation baselines.}
}