Can AI Agents Learn Their Way to the Top? Evaluating Heuristic Learning in a Long-Running Game Agent Competition

Key points

• AI agents offer an alternative by turning game experience into revisions of executable policies.1
• Building on heuristic learning (HL), we formalize Adversarial Heuristic Learning (AHL), a paradigm that uses AI agents as learning engines to refine game policies and supporting software while keeping model weights fixed.1
• We introduce AAArena, a benchmark comprising 12 authentic adversarial games and 1,920 archived human programs, with an evaluation protocol modeled on real-world game competitions.1
• Further experiments show that opponent selection and dense feedback support policy improvement, and that agents learn from both on-policy replays of their own matches and off-policy replays of other players' matches.1

Sources (2)

• [1]Can AI Agents Learn Their Way to the Top? Evaluating Heuristic Learning in a Long-Running Game Agent Competition
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 05:15 PM

  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.

• [2]Can AI Agents Learn Their Way to the Top? Evaluating Heuristic Learning in a Long-Running Game Agent Competition
  Hugging Face Daily Papers · Oct 8, 12:00 AM · same content

Extractive summary: sentences quoted from the sources.