  In statistical modelling the biggest threat is concept drift which makes the
model gradually showing deteriorating performance over time. There are state of
the art methodologies to detect the impact of concept drift, however general
strategy considered to overcome the issue in performance is to rebuild or
re-calibrate the model periodically as the variable patterns for the model
changes significantly due to market change or consumer behavior change etc.
Quantitative research is the most widely spread application of data science in
Marketing or financial domain where applicability of state of the art
reinforcement learning for auto-learning is less explored paradigm.
Reinforcement learning is heavily dependent on having a simulated environment
which is majorly available for gaming or online systems, to learn from the live
feedback. However, there are some research happened on the area of online
advertisement, pricing etc where due to the nature of the online learning
environment scope of reinforcement learning is explored. Our proposed solution
is a reinforcement learning based, true self-learning algorithm which can adapt
to the data change or concept drift and auto learn and self-calibrate for the
new patterns of the data solving the problem of concept drift.
  Keywords - Reinforcement learning, Genetic Algorithm, Q-learning,
Classification modelling, CMA-ES, NES, Multi objective optimization, Concept
drift, Population stability index, Incremental learning, F1-measure, Predictive
Modelling, Self-learning, MCTS, AlphaGo, AlphaZero
