  In this paper we address the solution of the popular Wordle puzzle, using new
reinforcement learning methods, which apply more generally to adaptive control
of dynamic systems and to classes of Partially Observable Markov Decision
Process (POMDP) problems. These methods are based on approximation in value
space and the rollout approach, admit a straightforward implementation, and
provide improved performance over various heuristic approaches. For the Wordle
puzzle, they yield on-line solution strategies that are very close to optimal
at relatively modest computational cost. Our methods are viable for more
complex versions of Wordle and related search problems, for which an optimal
strategy would be impossible to compute. They are also applicable to a wide
range of adaptive sequential decision problems that involve an unknown or
frequently changing environment whose parameters are estimated on-line.
