Code for Hands On Intelligent Agents with OpenAI Gym book to get started and learn to build deep reinforcement learning agents using PyTorch
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Updated
Jan 24, 2023 - Python
Code for Hands On Intelligent Agents with OpenAI Gym book to get started and learn to build deep reinforcement learning agents using PyTorch
👉 CARLA resources such as tutorial, blog, code and etc https://github.com/carla-simulator/carla
Multi-Agent Connected Autonomous Driving (MACAD) Gym environments for Deep RL. Code for the paper presented in the Machine Learning for Autonomous Driving Workshop at NeurIPS 2019:
Self Driving Cars Longitudinal and Lateral Control Design
Official github page of UCF SST CitySim Dataset
Deep Reinforcement Learning (PPO) in Autonomous Driving (Carla) [from scratch]
[NeurIPS 2022] Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline.
Code for the paper "Reinforced Curriculum Learning for Autonomous Driving in CARLA" (ICIP 2021)
TELECARLA: An Open Source Extension of the CARLA Simulator for Teleoperated Driving Research Using Off-the-Shelf Components
Saving incoming camera sensor images data as Numpy arrays to generate ground truth data for semantic segmentation
A simple gym environment wrapping Carla, a simulator for autonomous driving research. The environment is designed for developing and comparing reinforcement learning algorithms. Trackable costs also enable the application of safe reinforcement learning algorithms.
Learning Model Predictive Control (LMPC) for autonomous racing in CARLA 3D environment.
Implementation of a Longitudinal and Lateral controller (2D) of an autonomous vehicle on Carla Simulator
An implementation of a full motion and behavior planning pipeline for a self-driving car in the CARLA simulator.
Client example of Carla simulator with OpenCV in python
A simple yet effective repo for object detection based on the FCOS architecture.
Semantic Segmentation project for Autonomous Driving based on a TensorFlow implementation of UNet
Volatility plugins to recover ML model attributes from memory images
The goal of this project is to develop models capable of completing a variety of autonomous tasks within the Carla simulator.
This repo includes work on lidar point cloud semantic segmentation using self-collected Carla simulator dataset and Semantic KITTI real-world dataset.
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