Learn to navigate in dynamic environments with normalized LiDAR scans

TFM VIU - Master Inteligencia Artificial - Control de un dron usando AirSim Drone Racing Lab

code for `Autonomous navigation of UAV in multi-obstacle environments based on a Deep Reinforcement Learning approach'

Additional environments compatible with OpenAI gym

RL Dresden Algorithm Suite

Implementation of the Deep Deterministic Policy Gradient and Hindsight Experience Replay.

The source code of the [RA-L] paper "Reinforcement Learned Distributed Multi-Robot Navigation with Reciprocal Velocity Obstacle Shaped Rewards"

Implementation of the paper "Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning"

DSAC-v2; DSAC-T; DASC; Distributional Soft Actor-Critic

Deep Reinforcement Learning Based Crowd Navigation with Perceived Risk of the Moving Crowd for Mobile Robots

Official GitHub Repository for Efficient Off-Policy Safe Reinforcement Learning Using Trust Region Conditional Value At Risk.

This repository contains the codes for our paper titled "Risk-Aware Deep Reinforcement Learning for Robot Crowd Navigation".

[ICRA 2023] Adaptive and Explainable Deployment of Navigation Skills via Hierarchical Deep Reinforcement Learning

Risk Sensitive RL packages for continuous control

Integration of Fast-Planner and PX4-Avoidance with AirSim in UE4 environment

ROS-Jackal environment for RL

UAV Obstacle Avoidance using Deep Recurrent Reinforcement Learning with Temporal Attention

a simple kinematics mode of UAVs, and some thing else.

PyTorch implementation of Hierarchical Actor Critic (HAC) for OpenAI gym environments

A pytorch implementation of Constrained Reinforcement Learning Algorithm, including Constrained Soft Actor Critic (Soft Actor Critic Lagrangian) and Proximal Policy Optimization Lagrangian

新增一个CBF层，并将其结合进actor网络中，得到safe RL框架。后续验证中发现这种做法并没有实质性的用处，所以不再继续这个项目

obstacle detection and avoidance for UAV via gazebo

deep reinforcement learning for autonomous navigation