no code implementations • 3 May 2023 • Honoka Shiratori, Hiroaki Shinkawa, André Röhm, Nicolas Chauvet, Etsuo Segawa, Jonathan Laurent, Guillaume Bachelier, Tomoki Yamagami, Ryoichi Horisaki, Makoto Naruse
Quantum processes can realize conflict-free joint decisions among two agents using the entanglement of photons or quantum interference of orbital angular momentum (OAM).
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.
Second, to derive the optimal joint selection probability matrix, all players must disclose their probabilistic preferences.
Q-learning is a well-known approach in reinforcement learning that can deal with many states.
Here, we theoretically derive conflict-free joint decision-making that can satisfy the probabilistic preferences of all individual players.
In this study, we demonstrate a theoretical model to account for accelerating decision-making by correlated time sequence.
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.
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.
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.
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.