Single-Agent vs. Multi-Agent Techniques for Concurrent Reinforcement Learning of Negotiation Dialogue Policies
Code (0)
등록된 구현이 없습니다.
Tasks
Dialogue ManagementMulti-agent Reinforcement LearningQ-LearningReinforcement LearningReinforcement Learning (RL)Slot FillingSimilar Papers 제목 키워드 기반
Kimi K2.5: Visual Agentic Intelligence
We introduce Kimi K2.5, an open-source multimodal agentic model designed to advance general agentic intelligence. K2.5 emphasizes the joint optimization of text and vision so that two modalities enhance each other. This …
Reinforcement LearningPolyKV: A Shared Asymmetrically-Compressed KV Cache Pool for Multi-Agent LLM Inference
We present PolyKV, a system in which multiple concurrent inference agents share a single, asymmetrically compressed KV cache pool. Rather than allocating a separate KV cache per agent -- the standard paradigm -- PolyKV w…
AgentRoom: Concurrent Multi-Agent Coding in a CRDT-Backed Shared Workspace
Concurrent multi-agent coding promises division of labor across modules, robustness through redundancy, and parallel exploration at the natural granularity of multi-file projects. Realtime collaborative editing protocols…
Solving Multiagent Planning Problems with Concurrent Conditional Effects
In this work we present a novel approach to solving concurrent multiagent planning problems in which several agents act in parallel. Our approach relies on a compilation from concurrent multiagent planning to classical p…
Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability
Many real-world tasks involve multiple agents with partial observability and limited communication. Learning is challenging in these settings due to local viewpoints of agents, which perceive the world as non-stationary …
Multi-agent Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)