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Found 10 papers, 0 papers with code
A Lightweight Transmission Parameter Selection Scheme Using Reinforcement Learning for LoRaWAN
(1) Compared to other lightweight transmission-parameter selection schemes, collisions between LoRa devices can be efficiently avoided by our proposed scheme in LoRaWAN irrespective of changes in the available channels.
Resource allocation method using tug-of-war-based synchronization
We propose a simple channel-allocation method based on tug-of-war (TOW) dynamics, combined with the time scheduling based on nonlinear oscillator synchronization to efficiently use of the space (channel) and time resources in wireless communications.
A Balance for Fairness: Fair Distribution Utilising Physics in Games of Characteristic Function Form
In chaotic modern society, there is an increasing demand for the realization of true 'fairness'.
Ultrafast photonic reinforcement learning based on laser chaos
Reinforcement learning involves decision making in dynamic and uncertain environments, and constitutes one important element of artificial intelligence (AI).
Single photon in hierarchical architecture for physical reinforcement learning: Photon intelligence
In a past study, we successfully used the wave-particle duality of single photons to solve the two-armed bandit problem, which constitutes the foundation of reinforcement learning and decision making.
Category Theoretic Analysis of Photon-based Decision Making
Decision making is a vital function in this age of machine learning and artificial intelligence, yet its physical realization and theoretical fundamentals are still not completely understood.
Decision Maker based on Atomic Switches
We propose a simple model for an atomic switch-based decision maker (ASDM), and show that, as long as its total volume of precipitated Ag atoms is conserved when coupled with suitable operations, an atomic switch system provides a sophisticated "decision-making" capability that is known to be one of the most important intellectual abilities in human beings.
Harnessing Natural Fluctuations: Analogue Computer for Efficient Socially Maximal Decision Making
Our society comprises a collection of such individuals, and the society is expected to maximise the total rewards, while the individuals compete for common rewards.
Decision Maker using Coupled Incompressible-Fluid Cylinders
The multi-armed bandit problem (MBP) is the problem of finding, as accurately and quickly as possible, the most profitable option from a set of options that gives stochastic rewards by referring to past experiences.
Efficient Decision-Making by Volume-Conserving Physical Object
We demonstrate that any physical object, as long as its volume is conserved when coupled with suitable operations, provides a sophisticated decision-making capability.
