  Reinforcement Learning (RL) techniques have been increasingly applied in
optimizing control systems. However, their application in quantum systems is
hampered by the challenge of performing closed-loop control due to the
difficulty in measuring these systems. This often leads to reliance on assumed
models, introducing model bias, a problem that is exacerbated in open quantum
dynamics where Markovian approximations are not valid. To address these
challenges, we propose a novel approach that incorporates the non-Markovian
nature of the environment into a low-dimensional effective reservoir. By
initially employing a series of measurements as a 'dataset', we utilize machine
learning techniques to learn the effective quantum dynamics more efficiently
than traditional tomographic methods. Our methodology aims to demonstrates that
by integrating reinforcement learning with model learning, it is possible to
devise control policies and models that can counteract decoherence in a
spin-boson system. This approach may not only mitigates the issues of model
bias but also provides a more accurate representation of quantum dynamics,
paving the way for more effective quantum control strategies.
