No-Limit Texas Hold'em Poker agents created with evolutionary neural networks
Bibliographic record
Abstract
In order for computer poker agents to play the game well, they must analyse their current quality despite imperfect information, predict the likelihood of future game states dependent upon random outcomes, model opponents who are deliberately trying to mislead them, and manage finances to improve their current condition. This leads to a game space that is large compared to other classic games such as chess and backgammon. Evolutionary methods have been shown to find relatively good results in large state spaces, and neural networks have been shown to be able to find solutions to non-linear search problems such as poker. In this paper, we develop no-limit texas hold'em poker agents using a hybrid method known as evolving neural networks. We also investigate the appropriateness of evolving these agents using evolutionary heuristics such as co-evolution and halls of fame. Our agents were experimentally evaluated against several benchmark agents as well as agents previously developed in other work. Experimental results show the overall best performance was obtained by an agent evolved from a single population (i.e., no co-evolution) using a large hall of fame. These results demonstrate an effective use of evolving neural networks to create competitive no-limit texas hold'em poker agents.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".