arXiv AI

Reinforcement Learning Position Control of a Quadrotor Using Soft Actor-Critic (SAC)

arXiv:2512. 18333v2 Announce Type: replace-cross Abstract: This paper proposes a new Reinforcement Learning (RL) based control architecture for quadrotors.

arXiv AI

Jun 2

Control of a Twin Rotor using Twin Delayed Deep Deterministic Policy Gradient (TD3)

arXiv:2512. 13356v2 Announce Type: replace-cross Abstract: This paper proposes a reinforcement learning (RL) framework for controlling and stabilizing the Twin Rotor Aerodynamic System (TRAS) at specific pitch and azimuth angles and tracking a given trajectory.

By Zeyad Gamal, Youssef Mahran, Ayman El-Badawy

agents reinforcement-learning

arXiv AI

Jun 2

Dynamic Entropy Tuning in Reinforcement Learning Low-Level Quadcopter Control: Stochasticity vs Determinism

arXiv:2512. 18336v2 Announce Type: replace-cross Abstract: This paper explores the impact of dynamic entropy tuning in Reinforcement Learning (RL) algorithms that train a stochastic policy.

By Youssef Mahran, Zeyad Gamal, Ayman El-Badawy

reinforcement-learning

arXiv Machine Learning

Jul 1

Deep Reinforcement Learning for Spacecraft Attitude Control During Atmospheric Re-Entry

arXiv:2606. 31291v1 Announce Type: new Abstract: Deep reinforcement learning has the potential to solve attitude control problems more adaptively, precisely, and robustly by handling nonlinear dynamics, uncertainties, and failure cases more effectively than traditional attitude control approaches.

By Alexander Fabisch, Melvin Laux, Mariela De Lucas \'Alvarez, Edoardo Caroselli, Julian Theis

reinforcement-learning benchmarks

arXiv Machine Learning

Jul 23

Towards Torque-Driven Reinforcement Learning for Quadruped Locomotion

arXiv:2607. 18365v1 Announce Type: cross Abstract: Reinforcement learning (RL) for legged robots is advancing locomotion, demonstrating its ability to adapt to new and challenging terrain.

By Jordan Dowdy, Jean Chagas Vaz

agents reinforcement-learning robotics

arXiv Machine Learning

Jul 1

Machine Learning-based Feedback Linearization Control of Quadrotor Subject to Unmodeled Dynamics

arXiv:2606. 31199v1 Announce Type: cross Abstract: The control of agile quadrotors in dynamic and uncertain environments remains an open area of investigation to this day, particularly when the complete system dynamics are partially known or highly nonlinear.

By Amos Alwala, Gabriel da Silva Lima, Wallace Moreira Bessa

arXiv Machine Learning

Jul 7

Anticipatory Reinforcement Learning for Trajectory Tracking

arXiv:2607. 03132v1 Announce Type: new Abstract: Deep reinforcement learning (DRL) in industrial control often suffers from lag and overshoot due to purely reactive control based on the current tracking error.

By Georg Sch\"afer, Jakob Rehrl, Stefan Huber, Simon Hirlaender

reinforcement-learning robotics

arXiv Machine Learning

Jul 23

Isaac Sim-to-Real: Reinforcement Learning based Locomotion for Quadrupeds

arXiv:2607. 18135v1 Announce Type: cross Abstract: Learning-based approaches to locomotion have risen in popularity in recent years, showing the capability for complex legged locomotion and whole-body control.

By Jordan Dowdy, Jean Chagas Vaz

reinforcement-learning robotics

arXiv AI

Jun 2

Network Distributed Multi-Agent Reinforcement Learning for Consensus Control of Quadcopters

arXiv:2606. 02107v1 Announce Type: cross Abstract: This paper proposes a Network Distributed Multi-Agent Reinforcement Learning (ND-MARL) framework for quadcopter consensus control.

By Youssef Mahran, Zeyad Gamal, Aamir Ahmad, Ayman El-Badawy

agents reinforcement-learning fine-tuning

arXiv Machine Learning

Jun 2

Chebyshev Policies and the Mountain Car Problem: Reinforcement Learning for Low-Dimensional Control Tasks

arXiv:2605. 22305v2 Announce Type: replace Abstract: We analytically solve the Mountain Car problem, a canonical benchmark in RL, and derive an optimal control solution, closing a gap after 36 years.

By Stefan Huber, Hannes Unger, Georg Sch\"afer, Jakob Rehrl

agents reinforcement-learning benchmarks

arXiv AI

Jun 9

Self-Paced Curriculum Reinforcement Learning for Autonomous Superbike Racing in Simulation

arXiv:2606. 09236v1 Announce Type: cross Abstract: Autonomous Racing has seen remarkable progress through deep Reinforcement Learning (RL), primarily for four-wheeled vehicles.

By Luca Ghisi, Jacopo Essenziale, Carlo D'Eramo, Matteo Luperto

agents reinforcement-learning

arXiv Machine Learning

Jun 2

Trajectory Data Suffices for Statistically Efficient Policy Evaluation in Fixed-Horizon Offline RL with Linear $q^\pi$-Realizability and Concentrability

arXiv:2510. 03494v2 Announce Type: replace Abstract: We study finite-horizon offline reinforcement learning (RL) with function approximation for both policy evaluation and policy optimization.

By Volodymyr Tkachuk, Csaba Szepesv\'ari, Xiaoqi Tan

reinforcement-learning

arXiv Machine Learning

Jul 7

Integrating Physics-Informed Neural Networks for Safe Reinforcement Learning in a 1-DoF Helicopter System

arXiv:2607. 03125v1 Announce Type: new Abstract: Deep reinforcement learning (DRL) offers powerful control for industrial cyber-physical systems (ICPSs), but its "black-box" exploration risks violating strict hardware safety limits.

By Georg Sch\"afer, Jakob Rehrl, Stefan Huber

reinforcement-learning safety

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