  The game of Chinese Checkers is a challenging traditional board game of
perfect information that differs from other traditional games in two main
aspects: first, unlike Chess, all checkers remain indefinitely in the game and
hence the branching factor of the search tree does not decrease as the game
progresses; second, unlike Go, there are also no upper bounds on the depth of
the search tree since repetitions and backward movements are allowed.
Therefore, even in a restricted game instance, the state-space of the game can
still be unbounded, making it challenging for a computer program to excel. In
this work, we present an approach that effectively combines the use of
heuristics, Monte Carlo tree search, and deep reinforcement learning for
building a Chinese Checkers agent without the use of any human game-play data.
Experiment results show that our agent is competent under different scenarios
and reaches the level of experienced human players.
