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{this https URL}{Github}.

用全参数共享的多智能体强化学习（MARL）在中国跳棋的完全信息同质博弈中胜过独立和部分共享结构。为了运行实验，我们开发了一个新的 MARL 环境：可变大小、六人中国跳棋。该定制环境是在 PettingZoo 中开发的，支持游戏的所有传统规则，包括链式跳跃。据我们所知，这是中国跳棋的第一个忠实于真实游戏的实现。