/DDRL4NAV

We reproduce a light RL training framework from OpenAi Five. As seen in the following, the structure of our framework is totally the same as the paper shown. 3 key ingredients in the RL training process, Forward Module, Backward Module, and Env Module are separated.

Primary LanguagePythonGNU Affero General Public License v3.0AGPL-3.0

Distributed DRL Frame For Robot Navigation

Introduction

We reproduce a light RL training framework from OpenAi Five. As seen in the following, the structure of our framework is totally the same as the paper shown. 3 key ingredients in the RL training process, Forward Module, Backward Module, and Env Module are separated.

image-20220128203623248

ENVIRONMENT SETTING

recommend os : centos | ubuntu16.04+

initialize your working directory in the beginning

chsh -s /bin/bash    # make sure you are in a bash-based terminal
sudo mkdir -p  /home/${USER}/DDRL4NAV

Redis

if you have not installed redis-6.x yet, please type the following command in your terminal

wget https://download.redis.io/releases/redis-6.2.6.tar.gz
sudo tar -xzvf redis/redis-6.2.6.tar.gz -C /usr/local/
sudo ln -s /usr/local/redis-6.2.6/src/redis-server /usr/bin/redis-server
sudo apt install redis-tools

Python3.8

make sure your python version >= 3.8. (ubuntu20.04 bring it already)

third: create venv

cd /home/${USER}/DDRL4NAV
pip3 install --user virtualenv
python3.8 -m virtualenv venv38

forth: source venv

source venv38/bin/activate
pip install -r requirements.txt
# then install pytorch: 
# see https://pytorch.org/get-started/locally/
# pip+cenos+cuda11.3:
pip3 install torch==1.10.2+cu113 torchvision==0.11.3+cu113 torchaudio==0.10.2+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html

if you see (venv38) in the head of your terminal , it means installed venv successfully!

python
>>>import torch
>>>torch.cuda.is_available()
True

Notion: if you see False in your python console, just fit in pytorch version with your cuda .Torch Version

now , the whole project dir tree like this:

-DDRL4NAV
​    -venv38

Quick Start

supposed you are in /home/${USER}/DDRL4NAV dir

git clone git@github.com:DRL-Navigation/DDRL4NAV.git --recurse-submodules

now , the whole project dir tree like this:

-DDRL4NAV
​    -DDRL4NAV
​         -USTC_lab
​         -sh
​         -requirements.txt
​         -README.md
​    -venv38

now, let's try to train one classical atari game - Pong in your local machine

If you want to run robot navigation, you should also look nav_env guide to run ros node.

Notion: make sure to check USER in sh/config.sh

cd /home/${USER}/DDRL4NAV/DDRL4NAV/sh
bash start_redis.sh
bash start.sh

open tensorboard page :

bash tfboard.sh

stop training

# warmly stop
bash stop.sh
# or you can just kill them
ps -ef|grep main.py|grep -v grep|awk '{print $2}'|xargs kill -9

Connection between Env and Net

For training a neural network, you have to pick some kinds of states which observed by your env. In addition, you have to define encoding network to encode states, and put the encoded states to AC, Notice that LAST_INPUT_DIM dim in config_nn.py means the dim of encoded states which will flow into Actor and Critic later.

Distributed Training Start

Make a Jump Server first.

first: connect to Jump Server

second: update repo

you should have modify personal branch in your working machine

git clone git@github.com:DRL-Navigation/DDRL4NAV.git --recurse-submodules
git checkout -b XXX

deploy config/config.sh

modify sh/machines/all.sh

modify sh/envs/XX.sh  # XX.sh setting in sh/config.sh

git push origin XXX:XXX

In Jump Server

first time

USER=qiuqc # Notion: input your name here
mkdir -p /home/drl/{USER}/DDRL4NAV; cd /home/drl/${USER}/DDRL4NAV
git clone -b ${USER} git@git.ustc.edu.cn:drl_navigation/DDRL4NAV.git

then execute pull.sh to pull latest code in workers

cd /home/drl/${USER}/DDRL4NAV/DDRL4NAV/sh
bash pull.sh

third: start

cd /home/drl/${USER}/DDRL4NAV/DDRL4NAV/sh
bash start_redis.sh
bash start.sh config/config.sh remote

finally, open tensorboard then stop training if necessary

bash tfboard.sh remote
bash stop.sh remote

Jump Server file tree

-home
  -drl
    -UserA
      -DDRL4NAV
        -DDRL4NAV
          -output
    -UserB
      -DDRL4NAV
        -DDRL4NAV
          -output

Jump Server/跳板机

  • if you want to train in a distributed way, you should always connect to Jump Server before doing anything.

  • you should git push your personal branch before executing bash pull.sh remote in Jump Server

Supplement

  • Supports navigation tasks in different machines running in different environments, while using the same network for training. Note that currently only supports n processes running with same environment in one machine(worker). If necessary, this function can also be optimized into a machine with n processes running in n environments.

Other