Abstract: Reinforcement Learning (RL) has emerged as a powerful paradigm for optimizing control systems by enabling autonomous decision-making in dynamic environments. This review provides a ...

Robust Reinforcement Learning-based model for UAV self-separation under Uncertainty. Hybrid; Amsterdam , Noord-Holland , Netherlands; Aerosp ...

All results from 3 seeds × 18 test instances = 54 evaluation points. BO static outperforms PPO on small instances, but PPO overtakes at 500-variable scale. learned-control-layers/ ├── src/ │ ├── ...

Many enterprise RAG pipelines handle one type of search well and fail silently on the rest. Databricks on March 4 released a new agent called KARL, or Knowledge Agents via Reinforcement Learning, that ...

ABSTRACT: Oracle-based quantum algorithms cannot use deep loops because quantum states exist only as mathematical amplitudes in Hilbert space with no physical substrate. Critically, quantum wave ...

Agent Lightning is an agent optimization framework that enables agents to learn from their experiences through reinforcement learning and other methods. By treating agents as first-class citizens, ...

Explore the reinforcement learning algorithm that achieves performance comparable to GRPO in RLVR with minimal complexity. Learn how it works, why it’s effective, and its practical applications in RL ...

In reinforcement learning (RL), an agent learns to achieve its goal by interacting with its environment and learning from feedback about its successes and failures. This feedback is typically encoded ...

Add Decrypt as your preferred source to see more of our stories on Google. Social media platform X has open-sourced its Grok-based transformer model, which ranks For You feed posts by predicting user ...

Researchers at Google have developed a technique that makes it easier for AI models to learn complex reasoning tasks that usually cause LLMs to hallucinate or fall apart. Instead of training LLMs ...