SAUTE

Almost Surely Safe RL Using State Augmentation - Sauté RL

About

Satisfying safety constraints almost surely (or with probability one) can be critical for deployment of Reinforcement Learning (RL) in real-life applications. For example, plane landing and take-off should ideally occur with probability one. We address the problem by introducing Safety Augmented (Sauté) Markov Decision Processes (MDPs), where the safety constraints are eliminated by augmenting them into the state-space and reshaping the objective. We show that Sauté MDP satisfies the Bellman equation and moves us closer to solving Safe RL with constraints satisfied almost surely. We argue that Sauté MDP allows to view Safe RL problem from a different perspective enabling new features. For instance, our approach has a plug-and-play nature, i.e., any RL algorithm can be "sautéed". Additionally, state augmentation allows for policy generalization across safety constraints. In our experiments, we show that Sauté RL algorithms outperforms their state-of-the-art counterparts when constraint satisfaction is of high importance.

Installation

The following installation commands were tested in Ubuntu 18.04.

Create a conda environment

conda config --append channels conda-forge
conda env create -f sauterl.yml
conda activate sauterl

Our implementation is based on the Open AI safety starter agents. To install the Open AI libraries run the following commands:

mkdir safe-rl
cd safe-rl
git clone https://github.com/openai/safety-starter-agents.git && cd safety-starter-agents && pip install -e . && cd ..
git clone https://github.com/openai/safety-gym.git  && cd safety-gym && pip install -e . && cd ..
cd .. 

Install the remaining libraries

pip install -r pip_repos.txt

Sautéing an environment

Using our approach in practice is straightforward and requires just three steps:

1. Creating a safe environment
2. Sautéing the safe environment
3. Running a standard Reinforcement Learning algorithm

Creating a safe environment

In order to create a custom safe environment we need to define the safety cost and to inherit the rest of the definitions from the standard gym environment MyEnv and the provided class SafeEnv.

from envs.common.safe_env import SafeEnv

class MySafeEnv(SafeEnv, MyEnv):
    """New safety class"""
    def _safety_cost_fn(self, state:np.ndarray, action:np.ndarray, next_state:np.ndarray) -> np.ndarray:
        """Define the safety cost here."

The class SafeEnv contains the changes to the step method, which incorporates the safety constraints. Note that we assume that there is a method MyEnv._get_obs().

Sautéing a safe environment

Safety state augmentation (sautéing) is done in a straightforward manner. Assume a safe environment is defined in a class MySafeEnv. The sautéed environment is defined using a decorator saute_env, which contains all the required definitions. Custom and overloaded functions can be defined in the class body.

from envs.common.saute_env import saute_env

@saute_env
class MySautedEnv(MySafeEnv):
    """New sauteed class."""    

Running

We release a few tested safe environments, which can be evaluated using main.py. The file takes two arguments: the experiment identifier and the number of experiments for a particular algorithm to run in parallel. For instance,

python main.py --experiment 11 --num-exps 5

Our experiments:

ID	Environment	Algorithms	Type of Experiment
10	Pendulum swing-up	SAC, Langrangian SAC, Saute SAC	Performance
11	Pendulum swing-up	PPO, Langrangian PPO, Saute PPO	Performance
12	Pendulum swing-up	TRPO, Langrangian TRPO, Saute TRPO, CPO	Performance
13	Pendulum swing-up	Saute TRPO	Ablation
20	Double Pendulum	TRPO, Saute TRPO, CPO	Performance
21	Double Pendulum	Lagrangian TRPO	Performance
22	Double Pendulum	Saute TRPO	Naive generalization across safety budgets
23	Double Pendulum	Saute TRPO	Smart generalization across safety budgets
24	Double Pendulum	Saute TRPO	Ablation over unsafe reward value
30	Reacher	TRPO, Langrangian TRPO, Saute TRPO, CPO	Performance
40	Safety gym	TRPO, Langrangian TRPO, Saute TRPO, CPO	Performance

Output

By default the output is saved to ./logs/ directory (the directory can be modified in the method set_all_overrides in the BaseRunner class).

By default no checkpoints are saved, but the results are tracked in the tensorboard.

Citation

If you find our code useful please cite our paper!

@article{sootla2022saute,
  title={SAUT\'E RL: Almost Surely Safe Reinforcement Learning Using State Augmentation},
  author={Sootla, Aivar and Cowen-Rivers, Alexander I and Jafferjee, Taher and Wang, Ziyan and Mguni, David and Wang, Jun and Bou-Ammar, Haitham},
  journal={arXiv preprint arXiv:2202.06558},
  year={2022}
}