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5 results for “deep reinforcement learning”
SPOTting the Future: Lookahead Explanations for Deep Reinforcement Learning
Researchers introduced SPOT, a model-agnostic, sampling-based framework for generating lookahead explanations of deep reinforcement learning policies by constructing finite-horizon decision trees via environment simulation.
Aug 12, 2026
Vehicle routing problem using deep reinforcement learning - A case study about truck planning in the industry
A new arXiv preprint presents a deep reinforcement learning (DRL) approach to vehicle routing optimization across three industrial trucking use cases, reporting over 10% total cost reduction versus baseline methods.
Aug 10, 2026
Inference-Time Policy Alignment for Fair Reinforcement Learning
Researchers propose a new inference-time method to adjust pretrained reinforcement learning agents toward fairness objectives without retraining, enabling dynamic adaptation to stakeholder preferences post-deployment.
Aug 4, 2026
Principled Analysis of Deep Reinforcement Learning Evaluation and Design Paradigms
A new arXiv preprint critically examines foundational evaluation and design paradigms in deep reinforcement learning, demonstrating through large-scale experiments that widely accepted methodologies have led to incorrect conclusions about algorithm performance.
Jul 10, 2026
Safe and Adaptive Cloud Healing: Verifying LLM-Generated Recovery Plans with a Neural-Symbolic World Model
Researchers introduced PASE, a neuro-symbolic framework that uses LLMs to generate and verify cloud system recovery plans, claiming 40% faster recovery and improved fault detection on a real-world dataset.
Published Jul 3, 2026 · Analyzed Jul 6, 2026