184 research outputs found
[Escolanías] [Material gráfico]
Contiene fotografías pertenecientes al archivo fotográfico del diario "Región", publicadas entre 1976 y 1981Algunas fotos no indican autoría. El resto firmadas por Astra (Oviedo), y Foto Cine Alfonso Pascual (Noreña
Learning to Communicate Using Counterfactual Reasoning
This paper introduces a new approach for multi-agent communication learning
called multi-agent counterfactual communication (MACC) learning. Many
real-world problems are currently tackled using multi-agent techniques.
However, in many of these tasks the agents do not observe the full state of the
environment but only a limited observation. This absence of knowledge about the
full state makes completing the objectives significantly more complex or even
impossible. The key to this problem lies in sharing observation information
between agents or learning how to communicate the essential data. In this paper
we present a novel multi-agent communication learning approach called MACC. It
addresses the partial observability problem of the agents. MACC lets the agent
learn the action policy and the communication policy simultaneously. We focus
on decentralized Markov Decision Processes (Dec-MDP), where the agents have
joint observability. This means that the full state of the environment can be
determined using the observations of all agents. MACC uses counterfactual
reasoning to train both the action and the communication policy. This allows
the agents to anticipate on how other agents will react to certain messages and
on how the environment will react to certain actions, allowing them to learn
more effective policies. MACC uses actor-critic with a centralized critic and
decentralized actors. The critic is used to calculate an advantage for both the
action and communication policy. We demonstrate our method by applying it on
the Simple Reference Particle environment of OpenAI and a MNIST game. Our
results are compared with a communication and non-communication baseline. These
experiments demonstrate that MACC is able to train agents for each of these
problems with effective communication policies.Comment: Submitted to NeurIPS2020. Contains 10 pages with 9 figures and 4
appendice
Requirements and Specifications for the Orchestration of Network Intelligence in 6G
Next-generation mobile networks are expected to flaunt highly (if not fully) automated management. Network Intelligence (NI) will be the key enabler for such a vision, empowering myriad of orchestrators and controllers across network domains. In this paper, we elaborate on the DAEMON architectural model, which proposes introducing a NI Orchestration layer for the effective end-to-end coordination of NI instances deployed across the whole mobile network infrastructure. Specifically, we first outline requirements and specifications for NI design that stem from data management, control timescales, and network technology characteristics. Then, we build on such analysis to derive initial principles for the design of the NI Orchestration layer, focusing on (i) proposals for the interaction loop between NI instances and the NI Orchestrator, and (ii) a unified representation of NI algorithms based on an extended MAPE-K model. Our work contributes to the definition of the interfaces and operation of a NI Orchestration layer that foster a native integration of NI in mobile network architectures.This project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement no.101017109 DAEMON
Hierarchical temporal memory and recurrent neural networks for time series prediction : an empirical validation and reduction to multilayer perceptions
Evaluation of the IEEE 802.11ah Restricted Access Window Mechanism for dense IoT networks
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