Improving hearthstone AI by learning high-level rollout policies and bucketing chance node events
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Modern board, card, and video games are challenging domains for AI research due to their complex game mechanics and large state and action spaces. For instance, in Hearthstone - a popular collectible card (CC) (video) game developed by Blizzard Entertainment - two players first construct their own card decks from over 1,000 different cards and then draw and play cards to cast spells, select weapons, and combat minions and the opponent's hero. Players' turns are often comprised of multiple actions, including drawing new cards, which leads to enormous branching factors that pose a problem for state-of-the-art heuristic search methods. In this paper we first present two ideas to tackle this problem, namely by reducing chance node branching factors by bucketing events with similar outcomes, and using high-level policy networks for guiding Monte Carlo Tree Search rollouts. We then apply these ideas to the game of Hearthstone and show significant improvements over a state-of-the-art AI system for this game.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it