LearningRacer-rl

Overview

This software is able to self learning your AI Robocar by Deep reinforcement learning in few minutes.

You can use to Real Robocar and DonkeySim See in.

1. Description

Many DIY self driving car like JetBot or JetRacer, DonkeyCar are using behavior cloning by supervised-learning. The method need much labeled data that is collected by human demonstration. Human driving techniques is very important in this case.

On the other hands, In this software using deep reinforcement learning (DRL). That is can earned running behavior automatically through interaction with environment. Do not need sample data that is human labelling.

In addition this software agent can run on the Jetson Nano. Why can run on Jetson Nano and short learning time? because using integrate of SAC[soft actor critic] and VAE. SAC is a state of the art off-policy reinforcement learning method. In addition VAE train on cloud server beforehand as CNN layer of SAC.(This method called state representation learning) .

This method devised by Antonin RAFFIN
- Arrafine's Medium blog post
- Arrafine's implementsation for Simulator
Detail of SAC here:
- Google AI blog Soft Actor-Critic: Deep Reinforcement Learning for Robotics

2. Demo

This demo video showed that JetBot can earned policy of running road under 30 minutes. Only using Jetson Nano.

3. Setup

3.1 Requirements

Jetbot or JetRacer
JetPack>=4.2
Python=>3.6
pip>=19.3.1
pytorch>=1.8.0
Windows, macOS or Ubuntu (DonkeySim only)
x86-64 arch
Python>=3.6
pip>=19.3.1
DonkeySIM
Optional CUDA10.1(Windows and using GPU.)
pytorch>=1.8.0

3.2 Install

JetBot

Set up JetBot using the following SDCard image. [https://jetbot.org/v0.4.3/software_setup/sd_card.html]

Checking your JetBot Environment. Please write down JETBOT_VERSION and L4T_VERSION.

#JETBOT_VERSION
$ sudo docker images jetbot/jetbot | grep jupyter | cut -f 8 -d ' ' | cut -f 2 -d '-'

#L4T_VERSION
$ sudo docker images jetbot/jetbot | grep jupyter | cut -f 8 -d ' ' | cut -f 3 -d '-'

And Setup LearningRacer for Docker container image.

$ cd ~/ && git clone https://github.com/masato-ka/airc-rl-agent.git
$ cd airc-rl-agent/docker/jetbot && sh build.sh
$ sh enable.sh /home/jetbot

# disable jetbot/jetbot container. Tag name modify for your system by JETBOT_VERSION and L4T_VERSION.
$ sudo docker update --restart=no jetbot_jupyter
$ sudo restart

JetBot images(JetPack>=4.4) are using docker container . Therefore, build application on docker container . allocate maximum memory to the container.

You are able to use racer command inside docker container. Access to Jupyter Notebook on the container[http://:8888/] and launch terminal(File->new->terminal ).

You need train original VAE model. Because torch version problem. Coud you cahange to torch.save(vae.state_dict(), 'vae.torch', _use_new_zipfile_serialization=True) in VAE_CNN.ipynb training cell.

JetRacer.

Firstly setup your jetracer software to JetPack 4.5.1 following this link. Then run below command on your jetracer terminal.

$ cd ~/ && git clone https://github.com/masato-ka/airc-rl-agent.git
$ cd airc-rl-agent
$ sh install_jetpack.sh

Some time pytorch can not recognize your GPU by CUDA Driver problem. In this situation, you need to install pytorch following this link. Detail see in this

Other platform(DonkeySIM only).

$ cd ~/ && git clone https://github.com/masato-ka/airc-rl-agent.git
$ cd airc-rl-agent
$ sudo pip3 install .\[choose platform\]

You can choose platform from here
- windows
- windows-gpu
- osx
- ubuntu

When complete install please check run command.

$ racer --version
learning_racer version 1.5.0 .

4. Usage

4.1 JetBot and JetRacer

Create VAE Model

Collect 1k to 10 k images from your car camera using data_collection.ipynb or data_collection_without_gamepad.ipynbin notebook/utility/jetbot. If you use on JetRacer, usenotebook/utility/jetracer/data_collection.ipynb .
Learning VAE using VAE CNN.ipynb on Google Colaboratory.
Download vae.torch from host machine and deploy to root directory.

When your robot is Jetbot, Coud you modify VAE_CNN.ipynb.

final line trainng cell, Please change to True. ''' torch.save(vae.state_dict(), 'vae.torch', _use_new_zipfile_serialization=True) '''

Check and Evaluation

A.Offline check

When you run VAE_CNN.ipynb, you can check projection of latent spaces on TensorBoard Projection Tab. This latent spaces are labeled by K-means. If similar images stick together, it indicate to that good latent spaces.

B.Online check

Run notebooks/util/jetbot_vae_viewer.ipynb and Check reconstruction image. Check that the image is reconstructed at several places on the course.

If you use on JetRacer, Using jetracer_vae_viewer.ipynb .

Left is an actual image. Right is reconstruction image.
Color bar is represented latent variable of VAE(z=32 dim).

Start learning

Run user_interface.ipynb (needs gamepad). If you not have gamepad, use user_interface_without_gamepad.ipynb
Run train.py

$ racer train -robot jetbot
# If you use on JetRacer, "-robot jetracer". default is jetbot.

After few minutes, the AI car starts running. Please push STOP button immediately before the course out. Then, after `` `RESET``` is displayed at the prompt, press the START button. Repeat this.

When you use without_gamepad, you can check status using Validation box.

Can run	Waiting learning

racer train command options

Name	description	Default
-config(--config-path)	Specify the file path of config.yml.	config.yml
-vae(--vae-path)	Specify the file path of the trained VAE model.	vae.torch
-device(--device)	Specifies whether Pytorch uses CUDA. Set 'cuda' to use. Set 'cpu' when using CPU.	cuda
-robot(--robot-driver)	Specify the type of car to use. choose from jetbot, jetracer, jetbot-auto, jetracer-auto and sim.	JetBot
-steps(--time-steps)	Specify the maximum learning step for reinforcement learning. Modify the values according to the size and complexity of the course.	5000
-save_freq(--save_freq_episode)
Specify how many episodes to save the policy model. The policy starts saving after the gradient calculation starts.	10
-s(--save)	Specify the path and file name to save the model file of the training result.	model
-l(--load-model)	Define pre-train model path.	-

In -robot option, If you choose jetracer-auto or jetbot-auto, Auto train mode start. When this mode, Robot stop without human controll and pullback position where start learning.

Running DEMO

When only inference, run below command, The script load VAE model and RL model and start running your car.

$ racer demo -robot jetbot

racer demo command options

Name	description	Default
-config(--config-path)	Specify the file path of config.yml.	config.yml
-vae(--vae-path)	Specify the file path of the trained VAE model.	vae.torch
-model(--model-path	Specify the file to load the trained reinforcement learning model.	model
-device(--device)	Specifies whether Pytorch uses CUDA. Set 'cuda' to use. Set 'cpu' when using CPU.	cuda
-robot(--robot-driver)	Specify the type of car to use. JetBot and JetRacer can be specified.	JetBot
-steps(--time-steps)	Specify the maximum step for demo. Modify the values according to the size and complexity of the course.	5000
-tblog(--tb-log)	Define logging directory name, If not set, Do not logging.	None

In below command, run the demo 1000 steps with model file name is model.

$ racer demo -robot jetbot -steps 1000 -model model

4.1 Simulator

Download VAE model.

You can get pre-trained VAE model. from here

$wget "https://drive.google.com/uc?export=download&id=19r1yuwiRGGV-BjzjoCzwX8zmA8ZKFNcC" -O vae.torch

Start learning

$ racer train -robot sim -vae <downloaded vae model path> -device cpu -host <DonkeySim IP>

racer train options

Name	description	Default
-config(--config-path)	Specify the file path of config.yml.	config.yml
-vae(--vae-path)	Specify the file path of the trained VAE model.	vae.torch
-device(--device)	Specifies whether Pytorch uses CUDA. Set 'cuda' to use. Set 'cpu' when using CPU.	cuda
-robot(--robot-driver)	Specify the type of car to use. JetBot and JetRacer can be specified.	JetBot
-steps(--time-steps)	Specify the maximum learning step for reinforcement learning. Modify the values according to the size and complexity of the course.	5000
-save_freq(--save_freq_episode)	Specify how many steps to save the policy model. The policy starts saving after the gradient calculation starts.	10
-save_path(--save-model-path)	Specify the path for saved model file.	model_log
-s(--save)	Specify the path and file name to save the model file of the training result.	model
-l(--load-model)	Define pre-train model path.	-

Start Demo

$ racer demo -robot sim -model <own trained model path> -vae <downloaded vae model path> -steps 1000 -device cpu -host <DonkeySim IP> -user <your own name>

racer demo options

Name	description	Default
-config(--config-path)	Specify the file path of config.yml.	config.yml
-vae(--vae-path)	Specify the file path of the trained VAE model.	vae.torch
-model(--model-path	Specify the file to load the trained reinforcement learning model.	model
-device(--device)	Specifies whether Pytorch uses CUDA. Set 'cuda' to use. Set 'cpu' when using CPU.	cuda
-robot(--robot-driver)	Specify the type of car to use. JetBot and JetRacer can be specified.	JetBot
-steps(--time-steps)	Specify the maximum step for demo. Modify the values according to the size and complexity of the course.	5000
-user(--sim-user)	Define user name for own car that showed DonkeySim	anonymous
-car(--sim-car)	Define car model type for own car that showed DonkeySim	Donkey

5. Appendix

5.1 Configuration

You can configuration to some hyper parameter using config.yml.

Section	Parameter	Description
SAC_SETTING	LOG_INTERVAL	Reference to stable baselines document.
^	VERBOSE	^
^	LERNING_RATE	^
^	ENT_COEF	^
^	TRAIN_FREQ	^
^	BATCH_SIZE	^
^	GRADIENT_STEPS	^
^	LEARNING_STARTS	^
^	BUFFER_SIZE	^
^	GAMMA	^
^	TAU	^
^	USER_SDE	^
^	USER_SDE_AT_WARMUP	^
^	SDE_SAMPLE_FREQ	^
^	VARIANTS_SIZE	Define size of VAE latent
^	IMAGE_CHANNELS	Number of image channel.
REWARD_SETTING	REWARD_CRASH	Define reward when crash.
^	CRASH_REWARD_WEIGHT	Weight of crash reward.
^	THROTTLE_REWARD_WEIGHT	Weight of reward for speed.
AGENT_SETTING	N_COMMAND_HISTORY	Number of length command history as observation.
^	MIN_STEERING	min value of agent steering.
^	MAX_STEERING	max value of agent steering.
^	MIN_THROTTLE	min value of agent throttle.
^	MAX_THROTTLE	max value of agent throttle.
^	MAX_STEERING_DIFF	max value of steering diff each steps.
JETRACER_SETTING	STEERING_CHANNEL	Steering PWM pin number.
^	THROTTLE_CHANNEL	Throttle PWM pin number.
^	STEERING_GAIN	value of steering gain for NvidiaCar.
^	STEERING_OFFSET	value of steering offset for NvidiaCar.
^	THROTTLE_GAIN	value of throttle gain for NvidiaCar.
^	THROTTLE_OFFSET	value of throttle offset for NvidiaCar.

6. Release note

2020/03/08 Alpha release
- First release.
2020/03/16 Alpha-0.0.1 release
- Fix import error at jetbot_data_collection.ipynb.
2020/03/23 Beta release
- VAE Viewer can see latent space.
- Avoid stable_baseline source code change at install.
- train.py and demo.py merged to racer.py.
- Available without a game controller.
- Fix for can not copy dataset from google drive in CNN_VAE.ipynb
2020/03/23 Beta-0.0.1 release
- Fix VAE_CNN.ipynb (bug #18).
2020/04/26 v1.0.0 release
- Improvement install function.
- Can use DonkeySIM.
- YAML base configuration.
- Can use pre-trained model for SAC.
- Periodical saved model in each specific episode.
2020/06/30 v1.0.5 release
- BugFix
  - #20 Recording twice action in a step
  - #22 Error occurs when run demo subcommand in real car.
- Jetson nano install script improvement.
2020/10/11 v1.5.0 release
- BugFix
  - #25 Change interface for latest gym_donkey
- Migration to stable_baseline3(All Pytorch implementation)
- Improvement notebook.
  - user_interface.ipynb change UI.
  - VAE_CNN.ipynb a little faster training.
- You can use TensorBoard for monitoring training.
2021/01/09 v1.5.1 release
- BugFix
  - #32 Dose not working on Simulator environment.
  - README.md update.
2021/04/11 v1.5.2 release
- Corresponding to stable_baseline3 1.0
2021/12/26 v1.6.0 release
- BugFix
  - #33 Fix Can not stop over episode in simulator.
  - #40 Fix vae_viewer.ipynb is failed visualize reconstruction image color.
  - #42 Fix Can not load trained model.
  - #43 Fix VAE model problems.
- Improvement Notebook
  - Visualize latent space with TensorBoar Projection(VAE_CNN.ipynb)
- Improvement Function.
  - Docker installation for JetBot.
2022/03/27 v1.7.0 release
- BugFix
  - #46 In simurator crush reward fix.
- Improvement Function.
  - Release auto stop function without detail document.
- Other
  - VAE model is changed. VAE models are not backward compatible.
2022/07/03 v1.7.1 relase
- BugFix
  - #47 The learning_racer.vae decoder method is outputting the distributed image incorrectly.
  - #38 CNN_VAE.ipynb fix.
  - vae_viewer.ipynb fix.
  - Can not start command for internal errors.
- Inprovement Function.
  - Efficient hyperparameter in config.yml

7. Contribution

If you find bug or want to new functions, please write issue.
If you fix your self, please fork and send pull request.

LICENSE

This software license under MIT licence.

Author

masato-ka

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Repository files navigation

LearningRacer-rl

1. Description

2. Demo

3. Setup

3.1 Requirements

3.2 Install

4. Usage

4.1 JetBot and JetRacer

Create VAE Model

Check and Evaluation

Start learning

Running DEMO

4.1 Simulator

Download VAE model.

Start learning

Start Demo

5. Appendix

5.1 Configuration

6. Release note

7. Contribution

LICENSE

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content		content
docker/jetbot		docker/jetbot
learning_racer		learning_racer
notebooks		notebooks
LICENSE		LICENSE
README.md		README.md
config.yml		config.yml
install_jetpack.sh		install_jetpack.sh
install_jetpack_docker.sh		install_jetpack_docker.sh
setup.cfg		setup.cfg
setup.py		setup.py

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masato-ka/airc-rl-agent

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LearningRacer-rl

1. Description

2. Demo

3. Setup

3.1 Requirements

3.2 Install

4. Usage

4.1 JetBot and JetRacer

Create VAE Model

Check and Evaluation

Start learning

Running DEMO

4.1 Simulator

Download VAE model.

Start learning

Start Demo

5. Appendix

5.1 Configuration

6. Release note

7. Contribution

LICENSE

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