In my Pac-Man project, I've implemented search algorithms like depth-first, breadth-first, uniform cost, and A*. Explored multi-agent scenarios using minmax, alpha-beta pruning, expectimax. Also applied reinforcement learning with policy-based learning, Q-learning, and value iteration.
active 2024-05-05 → 2024-06-25 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 52 days from 2024-05-05 to 2024-06-25. Pushes: 9 total, peak 9 in a day. Pull requests: 0 total, peak 0 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 |
|---|---|---|---|---|
| kvamsid | 9 | 9 | 0 | 0 |
Recent activity
Latest issues, pull requests and releases
No issue or PR events — this repo's activity is pushes only.
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.