Skip to content
Conference

Partial End-to-End Reinforcement Learning of Soft-Actor-Critic Agents for Miniature Robot Car Racing

Jul 2026 · European Control Conference · pp. 2136-2141 · 0 citations · 16 references
Computer Science
View source

Similar papers

#reinforcement learning Conference Open access Sep 2026

A Review of Robot Adaptive Control Driven by Deep Reinforcement Learning

This review aims to clarify the development path of DRL-driven robot adaptive control, offering reference for theory, algorithm design, and engineering practice and it is hoped that this survey will help new researchers quickly grasp the landscape of the field.

Fu-Te Xiao · 0 citations
Conference Aug 2026

End-to-End Control of a Quadruped Robot Using Deep Reinforcement Learning

This paper presents the development and implementation of an end-to-end control framework for a quadruped walking robot based on deep reinforcement learning. The primary objective of the study is to design and verify a control system capable of autonomously generating locomotion strategies. A model of the walking robot...

Filip Połatyński, Paweł Skruch · 0 citations
#machine learning Preprint Sep 2026

Learning Multi-Agent Task Assignment and Navigation in the Factory: from Simulation to Real Robots

Feature-fusion Multi-Agent Proximal Policy Optimization (FMAPPO) is proposed, which fuses 2D LiDAR measurements with task-specific state information to enable safe decentralized multi-robot task assignment and navigation and demonstrates that the learned decentralized policies can coordinate multiple robots to service...

A. Abdalwhab, Giovanni Beltrame, David St-Onge · 0 citations
#reinforcement learning Review Open access Sep 2026

Recent Advances of Reinforcement Learning Algorithms for Autonomous Driving System

This survey examines RL-based AD in modular and end-to-end pipelines and relates reported methods to task formulation and deployment evidence and examines deployment barriers, including safety, Sim2Real generalization, data efficiency, computation, embodied alignment, and evaluation readiness.

B. Shuai, Min Hua, Le-Tian Tao et al. · 0 citations
Conference Open access Sep 2026

Self-Improving Autonomous Vehicles via Real-World Reinforcement Learning

This paper introduces a novel real-world RL algorithm that allows autonomous vehicles to collect informative scenarios and learn from them with minimal human intervention, and considers the learning progress of autonomous vehicles to identify informative scenarios and abort episodes before they enter unsafe states.

Daehyeok Kwon, Seung-Woo Seo, Sang-Hyun Lee · 0 citations
Preprint Aug 2026

Max-Q Selective Imitation for Human-in-the-Loop Online Robot Learning

A training method for HIL online reinforcement learning for real robots that automatically switches between learning from interventions and on-policy self-improvement, reducing the policy--target-sample gap that otherwise induces execution-time distribution shift.

Zihang Wang, Yishan Wang · 1 citation

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.