Can AI Agents Learn Their Way to the Top? Evaluating Heuristic Learning in a Long-Running Game Agent Competition
Claude Opus 5.5 with Claude Code won six of twelve AAArena adversarial game ladders.
The paper formalizes Adversarial Heuristic Learning, in which agents revise executable game policies and supporting software while model weights stay fixed. AAArena contains 12 authentic adversarial games and 1,920 archived human programs, and agents must interpret rules, select opponents, analyze replays, and revise agents within fixed budgets. Claude Opus 5.5 with Claude Code earned six gold medals, but no tested configuration led the remaining six human ladders. Opponent selection and dense feedback aided improvement, while complex rule specifications remained harder.
- AAArena covers 12 adversarial games and 1,920 archived human programs.
- Opus 5.5 with Claude Code earned six gold medals.
- No evaluated configuration topped the other six human ladders.
- Agents improved from both their own and other players' replays.
- Performance was weaker on games with more complex rules.
- Vendors
- Anthropic
- Products
- Claude CodeAAArena
- Organizations
- Anthropic
- AI models
- Claude Opus 5.5
From the original article
1 min read at huggingface.co
Adversarial games have driven advances from heuristic search to reinforcement learning, yet learning and adapting strategies from limited samples remain challenging. AI agents offer an alternative by turning game experience into revisions of executable policies.
By Kaisen Yang, Qingle Liu, Kejin Wang · huggingface.co
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