  Flexible implementations of Monte Carlo Tree Search (MCTS), combined with
domain specific knowledge and hybridization with other search algorithms, can
be powerful for finding the solutions to problems in complex planning. We
introduce mctreesearch4j, an MCTS implementation written as a standard JVM
library following key design principles of object oriented programming. We
define key class abstractions allowing the MCTS library to flexibly adapt to
any well defined Markov Decision Process or turn-based adversarial game.
Furthermore, our library is designed to be modular and extensible, utilizing
class inheritance and generic typing to standardize custom algorithm
definitions. We demonstrate that the design of the MCTS implementation provides
ease of adaptation for unique heuristics and customization across varying
Markov Decision Process (MDP) domains. In addition, the implementation is
reasonably performant and accurate for standard MDP's. In addition, via the
implementation of mctreesearch4j, the nuances of different types of MCTS
algorithms are discussed.

Machine Learning;Computation