OpenAi's gym environment wrapper to vectorize them with Ray

Overview

Ray Vector Environment Wrapper

You would like to use Ray to vectorize your environment but you don't want to use RLLib ?
You came to the right place !

This package allows you to parallelize your environment using Ray
Not only does it allows to run environments in parallel, but it also permits to run multiple sequential environments on each worker
For example, you can run 80 workers in parallel, each running 10 sequential environments for a total of 80 * 10 environments
This can be useful if your environment is fast and simply running 1 environment per worker leads to too much communication overhead between workers

Installation

pip install RayEnvWrapper

If something went wrong, it most certainly is because of Ray
For example, you might have issue installing Ray on Apple Silicon (i.e., M1) laptop. See Ray's documentation for a simple fix
At the moment Ray does not support Python 3.10. This package has been tested with Python 3.9.

How does it work?

You first need to define a function that seed and return your environment:

Here is an example for CartPole:

import gym

def make_and_seed(seed: int) -> gym.Env:
    env = gym.make('CartPole-v0')
    env = gym.wrappers.RecordEpisodeStatistics(env) # you can put extra wrapper to your original environment
    env.seed(seed)
    return env

Note: If you don't want to seed your environment, simply return it without using the seed, but the function you define needs to take a number as an input

Then, call the wrapper to create and wrap all the vectorized environment:

from RayEnvWrapper import WrapperRayVecEnv

number_of_workers = 4 # Usually, this is set to the number of CPUs in your machine
envs_per_worker = 2

vec_env = WrapperRayVecEnv(make_and_seed, number_of_workers, envs_per_worker)

You can then use your environment. All the output for each of the environments are stacked in a numpy array

Reset:

vec_env.reset()

Output

[[ 0.03073904  0.00145001 -0.03088818 -0.03131252]
 [ 0.03073904  0.00145001 -0.03088818 -0.03131252]
 [ 0.02281231 -0.02475473  0.02306162  0.02072129]
 [ 0.02281231 -0.02475473  0.02306162  0.02072129]
 [-0.03742824 -0.02316945  0.0148571   0.0296055 ]
 [-0.03742824 -0.02316945  0.0148571   0.0296055 ]
 [-0.0224773   0.04186813 -0.01038048  0.03759079]
 [-0.0224773   0.04186813 -0.01038048  0.03759079]]

The i-th entry represent the initial observation of the i-th environment
Note: As environments are vectorized, you don't need explicitly to reset the environment at the end of the episode, it is done automatically However, you need to do it once at the beginning

Take a random action:

vec_env.step([vec_env.action_space.sample() for _ in range(number_of_workers * envs_per_worker)])

Notice how the actions are passed. We pass an array containing an action for each of the environments
Thus, the array is of size number_of_workers * envs_per_worker (i.e., the total number of environments)

Output

(array([[ 0.03076804, -0.19321568, -0.03151444,  0.25146705],
       [ 0.03076804, -0.19321568, -0.03151444,  0.25146705],
       [ 0.02231721, -0.22019969,  0.02347605,  0.3205903 ],
       [ 0.02231721, -0.22019969,  0.02347605,  0.3205903 ],
       [-0.03789163, -0.21850128,  0.01544921,  0.32693872],
       [-0.03789163, -0.21850128,  0.01544921,  0.32693872],
       [-0.02163994, -0.15310344, -0.00962866,  0.3269806 ],
       [-0.02163994, -0.15310344, -0.00962866,  0.3269806 ]],
      dtype=float32), 
 array([1., 1., 1., 1., 1., 1., 1., 1.], dtype=float32), 
 array([False, False, False, False, False, False, False, False]), 
 [{}, {}, {}, {}, {}, {}, {}, {}])

As usual, the step method returns a tuple, except that here both the observation, reward, dones and infos are concatenated
In this specific example, we have 2 environments per worker.
Index 0 and 1 are environments from worker 1; index 1 and 2 are environments from worker 2, etc.

License

Apache License 2.0

You might also like...
A
A "gym" style toolkit for building lightweight Neural Architecture Search systems

A "gym" style toolkit for building lightweight Neural Architecture Search systems

Customizable RecSys Simulator for OpenAI Gym
Customizable RecSys Simulator for OpenAI Gym

gym-recsys: Customizable RecSys Simulator for OpenAI Gym Installation | How to use | Examples | Citation This package describes an OpenAI Gym interfac

Robot Servers and Server Manager software for robo-gym

robo-gym-server-modules Robot Servers and Server Manager software for robo-gym. For info on how to use this package please visit the robo-gym website

Deep Q Learning with OpenAI Gym and Pokemon Showdown

pokemon-deep-learning An openAI gym project for pokemon involving deep q learning. Made by myself, Sam Little, and Layton Webber. This code captures g

Manipulation OpenAI Gym environments to simulate robots at the STARS lab

Manipulator Learning This repository contains a set of manipulation environments that are compatible with OpenAI Gym and simulated in pybullet. In par

AI virtual gym is an AI program which can be used to exercise and can be used to see if we are doing the exercises

AI virtual gym is an AI program which can be used to exercise and can be used to see if we are doing the exercises

Multi-objective gym environments for reinforcement learning.
Multi-objective gym environments for reinforcement learning.

MO-Gym: Multi-Objective Reinforcement Learning Environments Gym environments for multi-objective reinforcement learning (MORL). The environments follo

Pytorch Lightning Distributed Accelerators using Ray

Distributed PyTorch Lightning Training on Ray This library adds new PyTorch Lightning accelerators for distributed training using the Ray distributed

Pytorch Lightning Distributed Accelerators using Ray

Distributed PyTorch Lightning Training on Ray This library adds new PyTorch Lightning plugins for distributed training using the Ray distributed compu

Comments
  • envs_per_worker

    envs_per_worker

    Hi!@ingambe. Thank you very much for your work! I have some questions. What does the "worker and envs" mean here? My understanding is as follows:

    • Worker represents a process. Two env in a worker belong to two threads.

    I don't know if I understand this correctly. Thanks! image

    opened by Meta-YZ 2
  • how to wrap two DIFFERENT environments?

    how to wrap two DIFFERENT environments?

    Thank you for upload the package. My question is is there a way to stack different environments together? For example I have ten or hundreds different race track environments and I want to train an agent simultaneously drive through this vectorized environment. In stable baseline I can stack them together and train a vectorized environment. Now I want to move to ray and try to speed up the training by using multiple gpu...but so far didn't figure out how to do this. Thanks in advance

    enhancement 
    opened by superfan123 1
Releases(v1.0)
Owner
Pierre TASSEL
Pierre TASSEL
Given a 2D triangle mesh, we could randomly generate cloud points that fill in the triangle mesh

generate_cloud_points Given a 2D triangle mesh, we could randomly generate cloud points that fill in the triangle mesh. Run python disp_mesh.py Or you

Peng Yu 2 Dec 24, 2021
POCO: Point Convolution for Surface Reconstruction

POCO: Point Convolution for Surface Reconstruction by: Alexandre Boulch and Renaud Marlet Abstract Implicit neural networks have been successfully use

valeo.ai 93 Dec 29, 2022
Various operations like path tracking, counting, etc by using yolov5

Object-tracing-with-YOLOv5 Various operations like path tracking, counting, etc by using yolov5

Pawan Valluri 5 Nov 28, 2022
This repository implements Douzero's interface to IGCA.

douzero-interface-for-ICGA This repository implements Douzero's interface to ICGA. ./douzero: This directory stores Doudizhu AI projects. ./interface:

zhanggenjin 4 Aug 07, 2022
The ICS Chat System project for NYU Shanghai Fall 2021

ICS_Chat_System [Catenger] This is the ICS Chat System project for NYU Shanghai Fall 2021 Creators: Shavarsh Melikyan, Skyler Chen and Arghya Sarkar,

1 Dec 20, 2021
GAN-based 3D human pose estimation model for 3DV'17 paper

Tensorflow implementation for 3DV 2017 conference paper "Adversarially Parameterized Optimization for 3D Human Pose Estimation". @inproceedings{jack20

Dominic Jack 15 Feb 27, 2021
StyleGAN2 with adaptive discriminator augmentation (ADA) - Official TensorFlow implementation

StyleGAN2 with adaptive discriminator augmentation (ADA) — Official TensorFlow implementation Training Generative Adversarial Networks with Limited Da

NVIDIA Research Projects 1.7k Dec 29, 2022
Neural Fixed-Point Acceleration for Convex Optimization

Licensing The majority of neural-scs is licensed under the CC BY-NC 4.0 License, however, portions of the project are available under separate license

Facebook Research 27 Oct 06, 2022
Image Classification - A research on image classification and auto insurance claim prediction, a systematic experiments on modeling techniques and approaches

A research on image classification and auto insurance claim prediction, a systematic experiments on modeling techniques and approaches

0 Jan 23, 2022
Working demo of the Multi-class and Anomaly classification model using the CLIP feature space

👁️ Hindsight AI: Crime Classification With Clip About For Educational Purposes Only This is a recursive neural net trained to classify specific crime

Miles Tweed 2 Jun 05, 2022
This codebase is the official implementation of Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization (NeurIPS2021, Spotlight)

Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization This codebase is the official implementation of Test-Time Classifier A

47 Dec 28, 2022
Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

PyTorch Implementation of Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers 1 Using Colab Please notic

Hila Chefer 489 Jan 07, 2023
3D2Unet: 3D Deformable Unet for Low-Light Video Enhancement (PRCV2021)

3DDUNET This is the code for 3D2Unet: 3D Deformable Unet for Low-Light Video Enhancement (PRCV2021) Conference Paper Link Dataset We use SMOID dataset

1 Jan 07, 2022
Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging

Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging This repository contains an implementation

Computational Photography Lab @ SFU 1.1k Jan 02, 2023
Datasets for new state-of-the-art challenge in disentanglement learning

High resolution disentanglement datasets This repository contains the Falcor3D and Isaac3D datasets, which present a state-of-the-art challenge for co

NVIDIA Research Projects 37 May 26, 2022
ProMP: Proximal Meta-Policy Search

ProMP: Proximal Meta-Policy Search Implementations corresponding to ProMP (Rothfuss et al., 2018). Overall this repository consists of two branches: m

Jonas Rothfuss 212 Dec 20, 2022
My tensorflow implementation of "A neural conversational model", a Deep learning based chatbot

Deep Q&A Table of Contents Presentation Installation Running Chatbot Web interface Results Pretrained model Improvements Upgrade Presentation This wor

Conchylicultor 2.9k Dec 28, 2022
Continuum Learning with GEM: Gradient Episodic Memory

Gradient Episodic Memory for Continual Learning Source code for the paper: @inproceedings{GradientEpisodicMemory, title={Gradient Episodic Memory

Facebook Research 360 Dec 27, 2022
A large-scale database for graph representation learning

A large-scale database for graph representation learning

Scott Freitas 29 Nov 25, 2022
Towards Flexible Blind JPEG Artifacts Removal (FBCNN, ICCV 2021)

Towards Flexible Blind JPEG Artifacts Removal (FBCNN, ICCV 2021)

Jiaxi Jiang 282 Jan 02, 2023