Name
Abstract | ||
|---|---|---|
The AlphaZero algorithm learns and plays games without hand-crafted expert knowledge. However, since its objective is to play well, we hypothesize that a better objective can be defined for the related but separate task of solving games. This paper proposes a novel approach to solving problems by modifying the training target of the AlphaZero algorithm, such that it prioritizes solving the game quickly, rather than winning. We train a Proof Cost Network (PCN), where proof cost is a heuristic that estimates the amount of work required to solve problems. This matches the general concept of the so-called proof number from proof number search, which has been shown to be well-suited for game solving. We propose two specific training targets. The first finds the shortest path to a solution, while the second estimates the proof cost. We conduct experiments on solving 15x15 Gomoku and 9x9 Killall-Go problems with both MCTS-based and FDFPN solvers. Comparisons between using AlphaZero networks and PCN as heuristics show that PCN can solve more problems. |
Year | Venue | Keywords |
|---|---|---|
2022 | International Conference on Learning Representations (ICLR) | Monte-Carlo Tree Search,Solving Games,AlphaZero,Deep Reinforcement Learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ti-Rong Wu | 1 | 1 | 1.71 |
| Chung-Chin Shih | 2 | 10 | 1.37 |
| tinghan wei | 3 | 10 | 7.89 |
| Meng-Yu Tsai | 4 | 0 | 0.34 |
| Wei-Yuan Hsu | 5 | 0 | 0.34 |
| I-Chen Wu | 6 | 208 | 55.03 |