Name
Title | ||
|---|---|---|
Towards Understanding Chinese Checkers with Heuristics, Monte Carlo Tree Search, and Deep Reinforcement Learning. |
Abstract | ||
|---|---|---|
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. |
Year | Venue | DocType |
|---|---|---|
2019 | arXiv: Learning | Journal |
Volume | Citations | PageRank |
abs/1903.01747 | 0 | 0.34 |
References | Authors | |
20 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ziyu Liu | 1 | 0 | 1.35 |
| Meng Zhou | 2 | 0 | 1.35 |
| Weiqing Cao | 3 | 0 | 0.34 |
| qiang qu | 4 | 83 | 12.15 |
| Henry Wing Fung Yeung | 5 | 24 | 3.01 |
| Vera Chung | 6 | 33 | 7.97 |