How does a gambler maximize winnings from a row of slot machines? This is the inspiration for the “multi-armed bandit problem,” a common task in reinforcement learning in which “agents” make choices to earn rewards. Recently, an international research team led by Hiroaki Shinkawa at the University of Tokyo developed an extended photonic reinforcement learning scheme that moves from the static bandit problem towards a more challenging dynamic environment. This study was published in Intelligent Computing.
New ‘bandit’ algorithm uses light for better bets
