2.0 KiB
Testing games
- Games are DLLs. Use a standalone test project to test a game.
- Create baseline game snapshots in textual format. Save to files.
- Create baseline logs via the R3 observable API. Save to files.
- Read the snapshot/logs manually to identify issues.
- Make sure serialization roundtrip works.
Play tests
The goal of playtests is to execute a game with AI players and potentially random seeds. Then the log/snapshots are read by the LLM agent to spot issues.
There is no failure in play tests; all tests pass but the agent should read the log for reference.
AI players are not LLMs here; they are typically common game AI implementations.
Create static functions that analyze the game state and return a command. Each such function is an agent. Each AI player routes its decisions to a random agent in a pool of weighted agents.
Agents to consider:
- Random: evenly chooses every move randomly.
- Greedy: uses a specific way to score actions. Always chooses the one with highest score. There can be multiple Greedy agents with different scoring.
For each playtest attempt, generate a full game log, analyze and report what you find from the log.
Round Runner
Playtests should run multiple rounds, not just one. The recommended pattern:
while chips >= minimum_bet AND chips < 2× starting_chips:
place bet → deal → agent decides hit/stand → resolve → new round
This exercises the full game lifecycle including deck reshuffling, round transitions, and chip management. Safety cap at ~200 rounds to prevent infinite loops.
Play Log Format
Each play log should contain:
- Header: agent name, seed, result summary, win/loss/push counts.
- Round Summaries: per-round result, chip delta, hit count.
- Decisions: per-decision hand total and choice (Hit/Stand).
- Reactivity: raw R3 observable log of component/entity changes.
- Final State: snapshot of the world after all rounds.
Logs are saved as .playlog files alongside test output for manual review.