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"

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.

Reinforcement learning for self-driving in a 3D simulation

Related tags

Overview

SelfDrive_AI

1. Requirements for the SelfDrive_AI Gym

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

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

Owner

Surajit Saikia

JAXMAPP: JAX-based Library for Multi-Agent Path Planning in Continuous Spaces

Memory efficient transducer loss computation

PyTorch framework, for reproducing experiments from the paper Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks

Realtime YOLO Monster Detection With Non Maximum Supression

A benchmark for the task of translation suggestion

Face recognition with trained classifiers for detecting objects using OpenCV

Disentangled Cycle Consistency for Highly-realistic Virtual Try-On, CVPR 2021

JUSTICE: A Benchmark Dataset for Supreme Court’s Judgment Prediction

Neural Ensemble Search for Performant and Calibrated Predictions

Character Grounding and Re-Identification in Story of Videos and Text Descriptions

fcn by tensorflow

Pytorch implementation for ACMMM2021 paper "I2V-GAN: Unpaired Infrared-to-Visible Video Translation".

Code for the paper SphereRPN: Learning Spheres for High-Quality Region Proposals on 3D Point Clouds Object Detection, ICIP 2021.

Implementation of temporal pooling methods studied in [ICIP'20] A Comparative Evaluation Of Temporal Pooling Methods For Blind Video Quality Assessment

Code for Understanding Pooling in Graph Neural Networks

A real world application of a Recurrent Neural Network on a binary classification of time series data

Running Google MoveNet Multipose Tracking models on OpenVINO.

SplineConv implementation for Paddle.

MMRazor: a model compression toolkit for model slimming and AutoML

Using Language Model to Bootstrap Human Activity Recognition Ambient Sensors Based in Smart Homes