  We show that multi-agent reinforcement learning (MARL) with full parameter
sharing outperforms independent and partially shared architectures in the
competitive perfect-information homogenous game of Chinese Checkers. To run our
experiments, we develop a new MARL environment: variable-size, six-player
Chinese Checkers. This custom environment was developed in PettingZoo and
supports all traditional rules of the game including chaining jumps. This is,
to the best of our knowledge, the first implementation of Chinese Checkers that
remains faithful to the true game.
  Chinese Checkers is difficult to learn due to its large branching factor and
potentially infinite horizons. We borrow the concept of branching actions
(submoves) from complex action spaces in other RL domains, where a submove may
not end a player's turn immediately. This drastically reduces the
dimensionality of the action space. Our observation space is inspired by
AlphaGo with many binary game boards stacked in a 3D array to encode
information.
  The PettingZoo environment, training and evaluation logic, and analysis
scripts can be found on
\href{https://github.com/noahadhikari/pettingzoo-chinese-checkers}{Github}.
