Facing the traveler tree, you wonder: which path shall I pick this time?
Choosing the right alternative in an uncertain world is not easy.
Advancing the multi-armed bandit theory will help you.
Providing algorithms with truely adaptive capabilities when facing an
unknown dynamics and environment. Reinforcement Learning is the basic formalism,
optimism in face of uncertainty a good tool, but
robustness, and adaptivity to the unknown structure are the real challenges.
The more applied you go, the stronger theory you need. This is an equilibrium,
between questions and answers, between dreams and practice.
Mathematics is the door and the key for optimisation,
learning guarantees and making your dreams come true.
As agroecology considers diverse scoring and variables of interest, often unmodeled,
identifying good practices requires massive on-field experimentation.
Advancing contextual group-sequential decision making strategies
may help massively scale up experimentation and recommendation.
Understanding the dynamics of complex systems, how to optimally act in them
can have a huge positive impact on all aspects of human societies that require
a careful management of natural, energetic, human and
computational resources. It is our duty to optimally answer it.
Choosing which future we want to shape is equally important as
picturing the world we dream of beyond the existing applications of current research.
From E-learning to Permaculture or Circular economy,
embrace the potential of sequential learning for our societies.
