arXiv Machine Learning August 17, 2026 By Anisa Saleem, Duksu Kim

CORAL: Curriculum-Optimized Reward Adaptation for LiDAR-Based Goal-Directed Urban Driving

Read the original on arXiv Machine Learning →

arXiv:2608. 14332v1 Announce Type: cross Abstract: Reinforcement learning is promising for autonomous urban driving, but long-horizon goal-directed navigation asks a policy to acquire several competing behaviors at once--reaching a distant goal, tracking a route, avoiding obstacles, obeying signals--and a fixed objective gives no order in which to learn them.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jul 10

Beyond Success Rates: Trainability and Extractability for Offline GCRL

arXiv:2602. 05459v2 Announce Type: replace Abstract: Offline goal-conditioned reinforcement learning (GCRL) is typically benchmarked by the best tuned success rate of each method.

By Jan Malte T\"opperwien, Aditya Mohan, Marius Lindauer

reinforcement-learning robotics

arXiv AI

Sep 21

AgenticRL: Agentic Reinforcement Learning with Self-Refinement for Complex UAV Navigation

arXiv:2606.03963v4 Announce Type: replace-cross Abstract: Deep reinforcement learning enables autonomous robots to learn complex navigation tasks, but still relies heavily on time consuming manual re...

By Roohan Ahmed Khan, Yasheerah Yaqoot, Amir Atef Habel, Muhammad Ahsan Mustafa, Dzmitry Tsetserukou

agents reinforcement-learning robotics fine-tuning multimodal safety

arXiv AI

Jul 15

TerraZero: Procedural Driving Simulation for Zero-Demonstration Self-Play at Scale

arXiv:2607. 13028v1 Announce Type: cross Abstract: Training robust autonomous driving agents requires a simulator that is fast enough for reinforcement learning at scale, realistic enough to ground behavior in real-world map structure, and diverse enough to cover the safety-critical long tail that logged data rarely contains.

By Zhouchonghao Wu, Akshay Rangesh, Weixin Li, Wei-Jer Chang, Zachary Lee, Tim Wang, Wei Zhan

agents reinforcement-learning robotics benchmarks safety

arXiv Machine Learning

Sep 18

MILER: Semantic Mid-Level Representation for Sim-to-Real Reinforcement Learning in Unstructured Autonomous Driving

MILER is an end‑to‑end reinforcement learning framework that achieves zero‑shot sim‑to‑real transfer for autonomous driving in unstructured environments. It uses a custom semantic mid‑level representation (MLR) simulator for offline training, and during deployment it processes real camera and LiDAR data with BEVFusion to produce a compatible bird’s‑eye‑view representation. The policy’s actions are applied via a trajectory‑alignment strategy, allowing the system to drive 17.3 km on a 3.0 km test track without human intervention, all running on a Jetson AGX Orin.

By Thomas Steinecker, Denis Trescher, Alexander Bienemann, Thorsten Luettel, Mirko Maehlisch

agents reinforcement-learning robotics safety

arXiv AI

Jun 6

AgenticRL: Self-Refining Agentic Reinforcement Learning for Vision-Conditioned UAV Navigation

arXiv:2606. 03963v2 Announce Type: replace-cross Abstract: Deep reinforcement learning has shown strong potential for enabling autonomous robots to learn complex navigational tasks.

By Roohan Ahmed Khan, Yasheerah Yaqoot, Muhammad Ahsan Mustafa, Dzmitry Tsetserukou

llms agents reinforcement-learning robotics fine-tuning multimodal

arXiv Machine Learning

Jun 25

Reward-Conditioned Attention: How Reward Design Shapes What Autonomous Driving Agents See

arXiv:2606. 25127v1 Announce Type: new Abstract: We investigate how reward design shapes the internal attention patterns of reinforcement learning agents trained for autonomous driving.

By Mohamed Benabdelouahad, Ahmed Djalal Hacini, Nadir Farhi, Aissa Boulmerka

agents reinforcement-learning robotics safety

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Related stories

Beyond Success Rates: Trainability and Extractability for Offline GCRL

AgenticRL: Agentic Reinforcement Learning with Self-Refinement for Complex UAV Navigation

TerraZero: Procedural Driving Simulation for Zero-Demonstration Self-Play at Scale

MILER: Semantic Mid-Level Representation for Sim-to-Real Reinforcement Learning in Unstructured Autonomous Driving

AgenticRL: Self-Refining Agentic Reinforcement Learning for Vision-Conditioned UAV Navigation

Reward-Conditioned Attention: How Reward Design Shapes What Autonomous Driving Agents See