SafePO-Baselines

Update !!!

We added a parameter to choose whether to use the discounted total cost to updateLagrange multiplier. Without any change to the previous command, you just need to add --use_discount_cost_update_lag True when run a command in terminal.

Safe Policy Optimization (SafePO) is a comprehensive algorithm benchmark for Safe Reinforcement Learning (Safe RL). It provides RL research community with a unified platform for processing and evaluating algorithms in various safe reinforcement learning environments. In order to better help the community study this problem, SafePO is developed with the following key features:

Comprehensive Safe RL benchmark: We offer high-quality implementation of both single-agent safe reinforcement learning algorithms (CPO, PCPO, FOCOPS, P3O, PPO-Lag, TRPO-Lag, PDO, CPPO-PID, RCPO, IPO, and SAC-Lag) and multi-agent safe reinforcement learning algorithms (HAPPO, MAPPO-Lag, IPPO, MACPO, and MAPPO).
Richer interfaces：In SafePO, you can modify the parameters of the algorithm according to your requirements. We provide customizable YAML files for each algorithm, and you can also pass in the parameters you want to change via argparse at the terminal.
Fairer and more effective：In the past, when comparing different algorithms, the number of interactions of each algorithm and the processing mode of buffer may be different. To solve this problem, we abstract the most basic Policy Gradient class and inherit all other algorithms from this class to ensure a fairer and more reasonable performance comparison. In order to improve efficiency, we also support multi-core CPU parallelization, which greatly accelerates algorithm development and verification.
More information：We provide rich data visualization methods. Reinforcement learning algorithms typically involves huge number of parameters. In order to better understand the changes of each parameter in the training process, we use log files, TensorBoard, and wandb to visualize them. We believe this will help developers tune each algorithm more efficiently.

SafePO-Baselines

Overview of Algorithms

Here we provide a table of Safe RL algorithms that the benchmark includes.

This work is currently under review. We have already implemented and tested five more algorithms: PDO, RCPO, CPPO-PID, IPO, SAC-Lag. We will add them into the repository as soon as possible.

Algorithm	Proceedings&Cites	Official Code Repo	Official Code Last Update	Official Github Stars
PPO-Lag	❌	Tensorflow 1
TRPO-Lag	❌	Tensorflow 1
FOCOPS	Neurips 2020 (Cite: 27)	Pytorch
CPO	ICML 2017(Cite: 663)	❌	❌	❌
PCPO	ICLR 2020(Cite: 67)	Theano	❌	❌
P3O	IJCAI 2022(Cite: 0)	❌	❌	❌
IPO	AAAI 2020(Cite: 47)	❌	❌	❌
PDO	❌	❌	❌	❌
RCPO	ICLR 2019 (Cite: 238)	❌	❌	❌
CPPO-PID	Neurips 2020(Cite: 71)	Pytorch
MACPO	Preprint(Cite: 4)	Pytorch
MAPPO-Lag	Preprint(Cite: 4)	Pytorch
HATRPO	ICLR 2022 (Cite: 10)	Pytorch
HAPPO (Purely reward optimisation)	ICLR 2022 (Cite: 10)	Pytorch
MAPPO (Purely reward optimisation)	Preprint(Cite: 98)	Pytorch
IPPO (Purely reward optimisation)	Preprint(Cite: 28)	❌	❌	❌

Supported Environments

For detailed instructions, please refer to Environments.md.

Pre-requisites

To use SafePO-Baselines, you need to install environments. Please refer to Mujoco, Safety-Gym, Bullet-Safety-Gym for more details on installation. Details regarding the installation of IsaacGym can be found here. We currently support the Preview Release 3 version of IsaacGym.

Conda-Environment

conda create -n safe python=3.8
conda activate safe
# because the cuda version, we recommend you install pytorch manual.
pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html
pip install -e .
conda install mpi4py
conda install scipy

For detailed instructions, please refer to Installation.md.

Getting Started

Single-Agent

train.py is the entrance file. Running train.py with arguments about algorithms and environments does the training. For example, to run PPO-Lag in Safexp-PointGoal1-v0, with 4 cpu cores and seed 0, you can use the following command:

python train.py --env-id Safexp-PointGoal1-v0 --algo ppo-lag --cores 4 --seed 0

Here we provide the list of common arguments:

Argument	Default	Info
--algo	required	the name of algorithm exec
--cores	int	the number of cpu physical cores you use
--seed	int	the seed you use
--check_freq	int: 25	check the snyc parameter
--entropy_coef	float:0.01	the parameter of entropy
--gamma	float:0.99	the value of dicount
--lam	float: 0.95	the value of GAE lambda
--lam_c	float: 0.95	the value of GAE cost lambda
--max_ep_len	int: 1000	unless environment have the default value else, we take 1000 as default value
--max_grad_norm	float: 0.5	the clip of parameters
--num_mini_batches	int: 16	used for value network tranining
--optimizer	Adam	the optimizer of Policy other : SGD, other class in torch.optim
--pi_lr	float: 3e-4	the learning rate of policy
--steps_per_epoch	int: 32000	the number of interactor steps
--target_kl	float: 0.01	the value of trust region
--train_pi_iterations	int: 80	the number of policy learn iterations
--train_v_iterations	int: 40	the number of value network and cost value network iterations
--use_cost_value_function	bool: False	use cost_value_function or not
--use_entropy	bool:False	use entropy or not
--use_reward_penalty	bool:False	use reward_penalty or not

Multi-Agent

We also provide a safe MARL algorithm benchmark for safe MARL research on the challenging tasks of Safety DexterousHands. HAPPO, IPPO, MACPO, MAPPO-Lag and MAPPO have already been implemented.

safepo/envs/safe_dexteroushands/train_marl.py is the entrance file. Running train_marl.py with arguments about algorithms and tasks does the training. For example, you can use the following command:

# algo: macpo, mappolag, mappo, ippo, happo
python train_marl.py --task=ShadowHandOver --algo=macpo

Selected Tasks

Base Environments	Description	Demo
ShadowHandOver	These environments involve two fixed-position hands. The hand which starts with the object must find a way to hand it over to the second hand.
ShadowHandCatchOver2Underarm	This environment is made up of half ShadowHandCatchUnderarm and half ShadowHandCatchOverarm, the object needs to be thrown from the vertical hand to the palm-up hand

We implement some different constraints to the base environments, expanding the setting to both single-agent and multi-agent.

What's More

Our team has also designed a number of more interesting safety tasks for two-handed dexterous manipulation, and this work will soon be releasing code for use by more Safe RL researchers.

Base Environments	Description	Demo
ShadowHandOverWall	None
ShadowHandOverWallDown	None
ShadowHandCatchOver2UnderarmWall	None
ShadowHandCatchOver2UnderarmWallDown	None

And the safe region are :

Wall	Wall Down

Machine Configuration

We test all algorithms and experiments on CPU: AMD Ryzen Threadripper PRO 3975WX 32-Cores and GPU: NVIDIA GeForce RTX 3090, Driver Version: 495.44.

Maintenance

This repo is under long-term maintenance of PKU-MARL team. We will keep adding new algorithms and supporting new environments as they come out. Please watch us and stay tuned!

Ethical and Responsible Use

SafePO aims to benefit safe RL community research, and is released under the Apache-2.0 license. Illegal usage or any violation of the license is not allowed.

PKU-MARL Team

The Baseline is a project contributed by MARL team at Peking University, please contact yaodong.yang@pku.edu.cn if you are interested to collaborate. We also thank the list of contributors of the following open source repositories: Spinning Up, Bullet-Safety-Gym, SvenG, Safety-Gym.

PKU-MARL/Safe-Policy-Optimization