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Databricks KARL Agent Tackles All Enterprise Search Types via RL

Databricks has released KARL, an RL-trained RAG agent that it says handles all six enterprise search categories at 33% lower cost than frontier models.

An AI-informed model of human reward-based learning: Hybrid approach could aid studies of mood disorders

People's decisions are known to be influenced by past experiences, including the outcomes of earlier choices. For over a century, psychologists have been trying to shed light on the processes ...

Databricks built a RAG agent it says can handle every kind of enterprise search

Databricks' KARL agent uses reinforcement learning to generalize across six enterprise search behaviors — the problem that breaks most RAG pipelines.
IEEE

Multi-Agent Deep Reinforcement Learning for Dynamic Routing in MANETs Using Graph Neural Networks

Abstract: Mobile Ad Hoc Networks (MANETs) consist of decentralized wireless networks with dynamic topologies and frequent link failures which make achieving efficient routing extremely difficult.
IEEE

Optimization of Joint Bidding Strategies in Integrated Electricity and Carbon Markets Using Multi-Agent Reinforcement Learning

Abstract: Under the guidance of the “dual carbon” strategic goal, the integration of the electricity and carbon markets is accelerating. Joint bidding strategies of market participants in this ...