Reinforcement learning for self-driving in a 3D simulation

Related tags

Deep Learning SelfDrive_AI
Overview

SelfDrive_AI

Reinforcement learning for self-driving in a 3D simulation (Created using UNITY-3D)

1. Requirements for the SelfDrive_AI Gym

You need Python 3.6 or later to run the simulation. (Note: the current environment is only supported in windows) Also, you can directly interact with the simulation by clicking the exe file and then by using W,A, S and D keys.

Please follow the two links below to install Unity-Gym and Stable-Baselines. Also, you can train it using your custom reinforcement learning algorithms by following the OpenAI gym structure (https://gym.openai.com/).

Install Unity-Gym

Install Stable-Baselines3

mlagents can be installed using pip:

$ python3 -m pip install mlagents

The image below illustrates the target goal of the AIcar, where the car needs to explore all the trajectories to find the bridge first.

2. (Training) You can train the environment by using the code below which has OpenAI gym structure. It will save the training results into a log directory which you can view using tensorboard. Feel free to change the parameters inside the code

from stable_baselines3 import PPO, SAC, ppo
from mlagents_envs.side_channel.engine_configuration_channel import EngineConfigurationChannel
channel = EngineConfigurationChannel()
from gym_unity.envs import UnityToGymWrapper
from mlagents_envs.environment import UnityEnvironment
import time,os
from stable_baselines3.common.vec_env import DummyVecEnv
from stable_baselines3.common.monitor import Monitor
from stable_baselines3.common.policies import ActorCriticPolicy
import math


env_name = "./UnityEnv"
speed = 15


env = UnityEnvironment(env_name,seed=1, side_channels=[channel])
channel.set_configuration_parameters(time_scale =speed)
env= UnityToGymWrapper(env, uint8_visual=False) # OpenAI gym interface created using UNITY

time_int = int(time.time())

# Diretories for storing results 
log_dir = "stable_results/Euler_env_3{}/".format(time_int)
log_dirTF = "stable_results/tensorflow_log_Euler3{}/".format(time_int) 
os.makedirs(log_dir, exist_ok=True)

env = Monitor(env, log_dir, allow_early_resets=True)
env = DummyVecEnv([lambda: env])  # The algorithms require a vectorized environment to run


model = PPO(ActorCriticPolicy, env, verbose=1, tensorboard_log=log_dirTF, device='cuda')
model.learn(int(200000)) # you can change the step size
time_int2 = int(time.time()) 
print('TIME TAKEN for training',time_int-time_int2)
# # save the model
model.save("Env_model")


# # # # # LOAD FOR TESTING
# del model
model = PPO.load("Env_model")

obs = env.reset()

# Test the agent for 1000 steps after training

for i in range(400):
    action, states = model.predict(obs)
    obs, rewards, done, info = env.step(action)
    env.render()

To monitor the training progress using tensorboard you type the following command from the terminal

$ tensorboard --logdir "HERE PUT THE PATH TO THE DIRECTORY"

Glimpse from the simulation environment

3. (Testing) The following code can be used to test the trained Humanoid Agent

from stable_baselines3 import PPO, SAC, ppo
from mlagents_envs.side_channel.engine_configuration_channel import EngineConfigurationChannel
channel = EngineConfigurationChannel()
from gym_unity.envs import UnityToGymWrapper
from mlagents_envs.environment import UnityEnvironment
import time,os
from stable_baselines3.common.vec_env import DummyVecEnv
from stable_baselines3.common.monitor import Monitor
from stable_baselines3.common.policies import ActorCriticPolicy
import math


env_name = "./UnityEnv"
speed = 1


env = UnityEnvironment(env_name,seed=1, side_channels=[channel])
channel.set_configuration_parameters(time_scale =speed)
env= UnityToGymWrapper(env, uint8_visual=False) # OpenAI gym interface created using UNITY

time_int = int(time.time())

# Diretories for storing results
log_dir = "stable_results/Euler_env_3{}/".format(time_int)
log_dirTF = "stable_results/tensorflow_log_Euler3{}/".format(time_int)
os.makedirs(log_dir, exist_ok=True)

env = Monitor(env, log_dir, allow_early_resets=True)
env = DummyVecEnv([lambda: env])  # The algorithms require a vectorized environment to run


model = PPO.load("Env_model")

obs = env.reset()

# Test the agent for 1000 steps after training

for i in range(1000):
    action, states = model.predict(obs)
    obs, rewards, done, info = env.step(action)
    env.render()

***Note: I am still developing the project by inducing more challenging constraints.

You might also like...
Conservative Q Learning for Offline Reinforcement Reinforcement Learning in JAX
Conservative Q Learning for Offline Reinforcement Reinforcement Learning in JAX

CQL-JAX This repository implements Conservative Q Learning for Offline Reinforcement Reinforcement Learning in JAX (FLAX). Implementation is built on

Karush Suri 8 Nov 7, 2022
Reinforcement-learning - Repository of the class assignment questions for the course on reinforcement learning

DSE 314/614: Reinforcement Learning This repository containing reinforcement lea

Manav Mishra 4 Apr 15, 2022
Simulation of self-focusing of laser beams in condensed media
Simulation of self-focusing of laser beams in condensed media

What is it? Program for scientific research, which allows to simulate the phenomenon of self-focusing of different laser beams (including Gaussian, ri

Evgeny Vasilyev 13 Dec 24, 2022
[ICML 2021] DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning | 斗地主AI
[ICML 2021] DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning | 斗地主AI

[ICML 2021] DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning DouZero is a reinforcement learning framework for DouDizhu (斗地主), t

Kwai Inc. 3.1k Jan 4, 2023
Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks

Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks This repository contains the code and data for the corresp

Friederike Metz 7 Apr 23, 2022
This solves the autonomous driving issue which is supported by deep learning technology. Given a video, it splits into images and predicts the angle of turning for each frame.
This solves the autonomous driving issue which is supported by deep learning technology. Given a video, it splits into images and predicts the angle of turning for each frame.

Self Driving Car An autonomous car (also known as a driverless car, self-driving car, and robotic car) is a vehicle that is capable of sensing its env

Sagor Saha 4 Sep 4, 2021
The Self-Supervised Learner can be used to train a classifier with fewer labeled examples needed using self-supervised learning.
The Self-Supervised Learner can be used to train a classifier with fewer labeled examples needed using self-supervised learning.

Published by SpaceML • About SpaceML • Quick Colab Example Self-Supervised Learner The Self-Supervised Learner can be used to train a classifier with

SpaceML 92 Nov 30, 2022
Repository to run object detection on a model trained on an autonomous driving dataset.
Repository to run object detection on a model trained on an autonomous driving dataset.

Autonomous Driving Object Detection on the Raspberry Pi 4 Description of Repository This repository contains code and instructions to configure the ne

Ethan 51 Nov 17, 2022
Official Repo for Ground-aware Monocular 3D Object Detection for Autonomous Driving

Visual 3D Detection Package: This repo aims to provide flexible and reproducible visual 3D detection on KITTI dataset. We expect scripts starting from

Yuxuan Liu 305 Dec 19, 2022
Owner
Surajit Saikia
Roboticist | PhD in AI | Deep learning, Reinforcement learning and Computer Vision.
Surajit Saikia
GitHub
Roach: End-to-End Urban Driving by Imitating a Reinforcement Learning Coach

CARLA-Roach This is the official code release of the paper End-to-End Urban Driving by Imitating a Reinforcement Learning Coach by Zhejun Zhang, Alexa

Zhejun Zhang 118 Dec 28, 2022
Self-Supervised Pillar Motion Learning for Autonomous Driving (CVPR 2021)

Self-Supervised Pillar Motion Learning for Autonomous Driving Chenxu Luo, Xiaodong Yang, Alan Yuille Self-Supervised Pillar Motion Learning for Autono

QCraft 101 Dec 5, 2022
Graph Self-Attention Network for Learning Spatial-Temporal Interaction Representation in Autonomous Driving

GSAN Introduction Code for paper GSAN: Graph Self-Attention Network for Learning Spatial-Temporal Interaction Representation in Autonomous Driving, wh

YE Luyao 6 Oct 27, 2022
Megaverse is a new 3D simulation platform for reinforcement learning and embodied AI research

Megaverse Megaverse is a new 3D simulation platform for reinforcement learning and embodied AI research. The efficient design of the engine enables ph

Aleksei Petrenko 191 Dec 23, 2022
Doosan robotic arm, simulation, control, visualization in Gazebo and ROS2 for Reinforcement Learning.

Robotic Arm Simulation in ROS2 and Gazebo General Overview This repository includes: First, how to simulate a 6DoF Robotic Arm from scratch using GAZE

David Valencia 12 Jan 2, 2023
Self Driving RC Car Code

Derp Learning Derp Learning is a Python package that collects data, trains models, and then controls an RC car for track racing. Hardware You will nee

Not Karol 39 Dec 7, 2022
Open source hardware and software platform to build a small scale self driving car.

Donkeycar is minimalist and modular self driving library for Python. It is developed for hobbyists and students with a focus on allowing fast experimentation and easy community contributions.

Autorope 2.4k Jan 4, 2023
Action Recognition for Self-Driving Cars

Action Recognition for Self-Driving Cars This repo contains the codes for the 2021 Fall semester project "Action Recognition for Self-Driving Cars" at

VITA lab at EPFL 3 Apr 7, 2022
Self-driving car env with PPO algorithm from stable baseline3

Self-driving car with RL stable baseline3 Most of the project develop from https://github.com/GerardMaggiolino/Gym-Medium-Post Please check it out! Th

Sornsiri.P 7 Dec 22, 2022
(CVPR 2022) A minimalistic mapless end-to-end stack for joint perception, prediction, planning and control for self driving.

LAV Learning from All Vehicles Dian Chen, Philipp Krähenbühl CVPR 2022 (also arXiV 2203.11934) This repo contains code for paper Learning from all veh

Dian Chen 300 Dec 15, 2022