Reinforcement learning and the effects of parameter settings in the game of Chung Toi
Bibliographic record
Abstract
This work applied reinforcement learning and the temporal difference TD(λ) algorithm to train a neural network to play the game of Chung Toi, a challenging variant of Tic-Tac-Toe. The effects of changing parameters and settings of the TD(λ) and of the neural network were evaluated by observing the ability of the network to learn the game of Chung Toi and play against a `smart' random player. This work applied techniques that have proven effective in training neural networks in general to the TD(λ) algorithm. The basic implementation of the TD(λ) method resulted in stable performance and achieved a maximal performance of winning 90.4% of evaluation games. When changing parameter settings, the best performance was achieved by using different learning rates between layers in the neural network (92.6% wins), and this was followed by using a relatively high probability of action exploitation (91.8% wins).
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".