Quantum simulations of a fiber squeezing experiment
We report on the excellent agreement of a first-principles, quantum dynamical simulation with the experimentally measured results of a fiber squeezer using intense, ultra-short laser pulses.
In applying reinforcement learning to continuous space problems, discretization or redefinition of the learning space can be a promising approach. Several methods and algorithms have been introduced to learning agents to respond to this problem. In our previous study, we introduced an FCCM clustering technique into Q-learning (called QL-FCCM) and its transfer learning in the Markov process. Since we could not respond to complicated environments like a non-Markov process, in this study, we propose a method in which an agent updates his Q-table by changing the trade-off ratio, Q-learning and QL-FCCM, based on the damping ratio. We conducted numerical experiments of the single pendulum standing problem and our model resulted in a smooth learning process.