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Asymmetric leader-laggard cluster synchronization for collective decision-making with laser network
Photonic accelerators have recently attracted soaring interest, harnessing the ultimate nature of light for information processing.
Conflict-free joint decision by lag and zero-lag synchronization in laser network
In this study, we explore the application of a laser network, acting as a photonic accelerator, to the competitive multi-armed bandit problem.
Asymmetric quantum decision-making
Quantum processes can realize conflict-free joint decisions among two agents using the entanglement of photons or quantum interference of orbital angular momentum (OAM).
Bandit Algorithm Driven by a Classical Random Walk and a Quantum Walk
Quantum walks (QWs) have a property that classical random walks (RWs) do not possess -- the coexistence of linear spreading and localization -- and this property is utilized to implement various kinds of applications.
Effect of temporal resolution on the reproduction of chaotic dynamics via reservoir computing
In recent years, reservoir computing has expanded to new functions such as the autonomous generation of chaotic time series, as well as time series prediction and classification.
Bandit approach to conflict-free multi-agent Q-learning in view of photonic implementation
In addition, we propose a multi-agent architecture in which agents are indirectly connected through quantum interference of light and quantum principles ensure the conflict-free property of state-action pair selections among agents.
Parallel photonic accelerator for decision making using optical spatiotemporal chaos
We solve a 512-armed bandit problem online, which is much larger than previous experiments by two orders of magnitude.
Conflict-free joint sampling for preference satisfaction through quantum interference
Second, to derive the optimal joint selection probability matrix, all players must disclose their probabilistic preferences.
Parallel bandit architecture based on laser chaos for reinforcement learning
Q-learning is a well-known approach in reinforcement learning that can deal with many states.
Controlling chaotic itinerancy in laser dynamics for reinforcement learning
In this paper, we propose a method for controlling the chaotic itinerancy in a multi-mode semiconductor laser to solve a machine learning task, known as the multi-armed bandit problem, which is fundamental to reinforcement learning.
Optimal preference satisfaction for conflict-free joint decisions
Here, we theoretically derive conflict-free joint decision-making that can satisfy the probabilistic preferences of all individual players.
Theory of Acceleration of Decision Making by Correlated Time Sequences
In this study, we demonstrate a theoretical model to account for accelerating decision-making by correlated time sequence.
Single-shot blind deconvolution with coded aperture
In this paper, we present a method for single-shot blind deconvolution incorporating a coded aperture (CA).
Conflict-free collective stochastic decision making by orbital angular momentum of photons through quantum interference
In recent cross-disciplinary studies involving both optics and computing, single-photon-based decision-making has been demonstrated by utilizing the wave-particle duality of light to solve multi-armed bandit problems.
Arm order recognition in multi-armed bandit problem with laser chaos time series
By exploiting ultrafast and irregular time series generated by lasers with delayed feedback, we have previously demonstrated a scalable algorithm to solve multi-armed bandit (MAB) problems utilizing the time-division multiplexing of laser chaos time series.
Adaptive model selection in photonic reservoir computing by reinforcement learning
Here, we propose a scheme of adaptive model selection in photonic reservoir computing using reinforcement learning.
Lotka-Volterra competition mechanism embedded in a decision-making method
Decision making is a fundamental capability of living organisms, and has recently been gaining increasing importance in many engineering applications.
Generative adversarial network based on chaotic time series
Here we utilize chaotic time series generated experimentally by semiconductor lasers for the latent variables of GAN whereby the inherent nature of chaos can be reflected or transformed into the generated output data.
Entangled-photon decision maker
The competitive multi-armed bandit (CMAB) problem is related to social issues such as maximizing total social benefits while preserving equality among individuals by overcoming conflicts between individual decisions, which could seriously decrease social benefits.
Local reservoir model for choice-based learning
Our proposed model is inspired by the viewpoint that a decision is affected by its local environment, which is referred to as a local reservoir.
Scalable photonic reinforcement learning by time-division multiplexing of laser chaos
In this study, we demonstrated a scalable, pipelined principle of resolving the multi-armed bandit problem by introducing time-division multiplexing of chaotically oscillated ultrafast time-series.
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.
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.
