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17 search results found

Gym Pybullet Drones ⭐ 931

PyBullet Gym environments for single and multi-agent reinforcement learning of quadcopter control

Uav Ddpg ⭐ 297

Code for paper "Computation Offloading Optimization for UAV-assisted Mobile Edge Computing: A Deep Deterministic Policy Gradient Approach"

Uav_obstacle_avoiding_drl ⭐ 242

This is a project about deep reinforcement learning autonomous obstacle avoidance algorithm for UAV.

A universal flight control tuning framework

Rl_ardrone ⭐ 115

Autonomous Navigation of UAV using Reinforcement Learning algorithms.

Airsimdrl ⭐ 68

Autonomous UAV Navigation without Collision using Visual Information in Airsim

Autonomous_drone_for_tracking ⭐ 36

Autonomous Drone for Object Tracking

Event Vae Rl ⭐ 34

Visuomotor policies from event-based cameras through representation learning and reinforcement learning. Accompanies our paper: https://arxiv.org/abs/2103.00806

Gym_multirotor ⭐ 25

Multi-rotor Gym

Logisticsenv ⭐ 23

UAV Logistics Environment for Multi-Agent Reinforcement Learning / Unity ML-Agents / Unity 3D

Rl_exploration_for_uavs ⭐ 19

Reinforcement Learning-based exploration algorithm to drive a fleet of UAVs in an unknown environment.

Optimizing Uav Trajectory For Maximum Data Rate Via Q Learning ⭐ 15

During our participation in the Internship Exchange Program, my friend and I collaborated with the guidance of our esteemed supervisor from NTHU.

Drone Hover Rl ⭐ 12

A hover system for Microsoft AirSim using reinforcement learning (Q-learning and SARSA)

Uav Based Cellular Communication Multi Agent Drl Solution ⭐ 12

UAV-based Cellular-Communication: Multi-Agent Deep Reinforcement Learning for Interference Management

Multi_rotor_avoidance_rl ⭐ 11

DRL-based path planner for real quadrotor

Learningtofly ⭐ 6

[SIGGRAPH 2019] Learning to Fly: Computational Controller Design for Hybrid UAVs with Reinforcement Learning

Experimenting with meta-learning approaches to opponent modelling in MARL. Building upon previous public implementations of MADDPG and M3DDPG.

Reinforcement_learning_team_q_learnig_marl_multi_agent_uav_spectrum_task ⭐ 5

A solution for Dynamic Spectrum Management in Mission-Critical UAV Networks using Team Q learning as a Multi-Agent Reinforcement Learning Approach

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