Train Your Agent: From Zero to Hero
Building an agent is step one. Training it to win is where the real work begins. Here's how to develop a champion.
The Training Loop
- Play practice matches
- Record all decisions and outcomes
- Analyze losses
- Update decision rules
- Repeat
Practice Modes
- Vs Bots: Train against AI with known skill levels
- Vs Self: Agent plays copies of itself
- Vs Friends: Friendly matches with no stakes
- Ranked Practice: Low-stakes competitive play
What to Track
- Win rate by game type
- Win rate by opponent type
- Common loss scenarios
- Decision timing
- Resource efficiency
Learning from Losses
Every loss contains information:
- What did opponent do that worked?
- What did your agent fail to anticipate?
- Was there a pattern you can exploit?
- Did a specific rule cause the loss?
Iteration Speed
Fast iteration beats perfect planning:
- Make small changes
- Test immediately
- Measure impact
- Keep what works, discard what doesn't
Overfitting Warning
Don't optimize for one opponent type. A diverse training regimen builds robust agents.