Self Driving with CARLA - Part 1: Steering with Deep Q Learning Networks

Автор: Jabrah Tutorials

Загружено: 2023-06-17

Просмотров: 10937

Описание:

#selfdriving, #carla, #qlearning, #pytorch
In this lecture we enter the realm of autonomous driving by making use of the Carla simulator and implementing deep q-learning networks in pytorch. The task is to control the steering of a self driving agent. This lecture is only an introduction to doing self driving in this manner (for end to end autonomous systems), and as we learn more reinforcement learning & deep learning algorithms, we'll improve our agent's performance. Stay tuned.

You can find the code referenced in this lecture in the following repository: https://github.com/JabrahTutorials/Re...

The agent was trained for about 7-8 hours, just to show its capability to learn in this environment.

Reference: This paper inspired the approach taken
https://link.springer.com/article/10....

0:00 Intro
1:50 Problem Setup
6:20 Reward Function Design
16:45 Image Processing
20:45 Architecture
29:40 Challenges in RL Design
34:20 Programming Solution

Self Driving with CARLA - Part 1: Steering with Deep Q Learning Networks

Доступные форматы для скачивания:

Скачать видео mp4

Информация по загрузке:

Скачать аудио mp3

Похожие видео

Reinforcement Learning 20 - Off Policy Learning with Approximation

Reinforcement Learning 20 - Off Policy Learning with Approximation

CARLA Autonomous Driving Challenge 2020: Talks

CARLA Autonomous Driving Challenge 2020: Talks

lostdesign/linked - Gource visualisation

lostdesign/linked - Gource visualisation

The FASTEST introduction to Reinforcement Learning on the internet

The FASTEST introduction to Reinforcement Learning on the internet

1767805154C++ Basics for Beginners | Variables, Constants, Data Types & Input/Output | Part 1

1767805154C++ Basics for Beginners | Variables, Constants, Data Types & Input/Output | Part 1

Leetcode 1458 | Max Dot Product of Two Subsequences | Optimized Solution + Code

Leetcode 1458 | Max Dot Product of Two Subsequences | Optimized Solution + Code

Я в опасности

Autonomous Vehicle Technical Stack

Autonomous Vehicle Technical Stack

Deep Learning 5 - Simple Convolutional Neural Networks in Pytorch

Deep Learning 5 - Simple Convolutional Neural Networks in Pytorch

Как происходит модернизация остаточных соединений [mHC]

Как происходит модернизация остаточных соединений [mHC]

Sensor Fusion for Autonomous Vehicles: Strategies, Methods, and Tradeoffs | Synopsys

Sensor Fusion for Autonomous Vehicles: Strategies, Methods, and Tradeoffs | Synopsys

Making Real-World Reinforcement Learning Practical

Making Real-World Reinforcement Learning Practical

Programming Autonomous self-driving cars with Carla and Python

Programming Autonomous self-driving cars with Carla and Python

An Introduction to Graph Neural Networks: Models and Applications

An Introduction to Graph Neural Networks: Models and Applications

Обучение с подкреплением с нуля

Обучение с подкреплением с нуля

Deep Learning 2 - Intro to Neural Networks (Backpropagation)

Deep Learning 2 - Intro to Neural Networks (Backpropagation)

Build Your First Pytorch Model In Minutes! [Tutorial + Code]

Build Your First Pytorch Model In Minutes! [Tutorial + Code]

Proximal Policy Optimization (PPO) is Easy With PyTorch | Full PPO Tutorial

Proximal Policy Optimization (PPO) is Easy With PyTorch | Full PPO Tutorial

MIT 6.S191 (2024): Reinforcement Learning

MIT 6.S191 (2024): Reinforcement Learning

Алгоритм памяти, вдохновлённый работой мозга

Алгоритм памяти, вдохновлённый работой мозга