Realtime reinforcement learning: Towards rapid asynchronous deployment of large models
Realtime environments change even as agents perform action inference and learning, thus
requiring high interaction frequencies to effectively minimize long-term regret. However …
requiring high interaction frequencies to effectively minimize long-term regret. However …
Cooperative ai via decentralized commitment devices
Credible commitment devices have been a popular approach for robust multi-agent
coordination. However, existing commitment mechanisms face limitations like privacy …
coordination. However, existing commitment mechanisms face limitations like privacy …
Enabling Realtime Reinforcement Learning at Scale with Staggered Asynchronous Inference
Realtime environments change even as agents perform action inference and learning, thus
requiring high interaction frequencies to effectively minimize regret. However, recent …
requiring high interaction frequencies to effectively minimize regret. However, recent …
Can Large Language Models Adapt to Other Agents In-Context?
As the research community aims to build better AI assistants that are more dynamic and
personalized to the diversity of humans that they interact with, there is increased interest in …
personalized to the diversity of humans that they interact with, there is increased interest in …
The Danger Of Arrogance: Welfare Equilibra As A Solution To Stackelberg Self-Play In Non-Coincidental Games
The increasing prevalence of multi-agent learning systems in society necessitates
understanding how to learn effective and safe policies in general-sum multi-agent …
understanding how to learn effective and safe policies in general-sum multi-agent …
[PDF][PDF] Hierarchical Tool Management: Structuring Active Solutions in Large Language Models
M Gupta, K Mehta, A Nair, S Desai, P Singh - 2024 - researchgate.net
Large language models (LLMs) have revolutionized various domains through their ability to
process and generate human-like text. However, effectively managing and retrieving tools …
process and generate human-like text. However, effectively managing and retrieving tools …
[ספר][B] Effective Learning in Non-Stationary Multiagent Environments
DK Kim - 2023 - search.proquest.com
Multiagent reinforcement learning (MARL) provides a principled framework for a group of
artificial intelligence agents to learn collaborative and/or competitive behaviors at the level of …
artificial intelligence agents to learn collaborative and/or competitive behaviors at the level of …