Implement model-based algorithms, such as Monte Carlo Tree Search (MCTS) or minimax, to construct a search tree and explore the game's state space. Evaluate and compare the performance of model-based and model-free algorithms (Q-learning, PPO) in terms of efficiency, optimality, and adaptability to different game scenarios.
active 2024-04-29 → 2024-07-25 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 88 days from 2024-04-29 to 2024-07-25. Pushes: 4 total, peak 4 in a day. Pull requests: 5 total, peak 3 in a day. Issues: 0 total, peak 0 in a day. Comments: 0 total, peak 0 in a day. Stars: 1 total, peak 1 in a day.
- Pushes
- Pull requests
- Issues
- Comments
- Stars
Top contributors
Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity
| Contributor | Contributions | Pushes | PRs | Comments |
|---|---|---|---|---|
| dependabot[bot] | 8 | 3 | 5 | 0 |
| JaninaMattes | 1 | 1 | 0 | 0 |
Recent activity
Latest issues, pull requests and releases
Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 1 stars here means stars gained during the window, not the repo's star count.