Board Game Models
AlphaZero
Introduced by Silver et al. in Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning AlgorithmAlphaZero is a reinforcement learning agent for playing board games such as Go, chess, and shogi.
Source: Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Reinforcement Learning (RL) | 21 | 24.42% |
| Board Games | 18 | 20.93% |
| Game of Go | 8 | 9.30% |
| Atari Games | 7 | 8.14% |
| Decision Making | 6 | 6.98% |
| Game of Chess | 6 | 6.98% |
| Combinatorial Optimization | 2 | 2.33% |
| Multi-agent Reinforcement Learning | 2 | 2.33% |
| Test | 2 | 2.33% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
