We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| TheVGLC/TheVGLC | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| astar_shortest_path | Ran | TheVGLC/TheVGLC/PlatformerPathfinding/pathfinding.py code served (permissive licence) · get_code("84dfb35b354d5ba0") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Levels are a key component of many different video games, and a large body of work has been produced on how to procedurally generate game levels. Recently, Machine Learning techniques have been applied to video game level generation towards the purpose of automatically generating levels that have the properties of the training corpus. Towards that end we have made available a corpora of video game levels in an easy to parse format ideal for different machine learning and other game AI research purposes.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1606.07487")
get_code_for_paper("1606.07487")
have("1606.07487")
Connect an agent — have() is free.