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}.

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.

Recent research on external-memory search has shown that disks can be effectively usedas secondary storage when performing large breadth-first searches.We introduce the Write-Minimizing Breadth-First Search (WMBFS) algorithm which is designed to minimizethe number of writes performed in an external-memory BFS. WMBFS is also designed to store the results ofthe BFS for later use.We present the results of a BFS on a single-agent version of Chinese Checkers and the Rubik's Cube edge cubes, state spaceswith about 1 trillion states each. In evaluating against a comparable approach, WMBFS reduces the I/O for the Chinese Checkers domain by over an order of magnitude.In Rubik's cube, in addition to reducing I/O, the search is also 3.5 times faster.Analysis of the results suggests the machine and state-space properties necessary for WMBFS to perform well.