Bibliographic record
Abstract
A key challenge in programming virtual environments is to produce virtual characters that are autonomous and capable of action selections that appear believable. In this chapter, motivations are used as a basis for learning using reinforcements. With motives driving the decisions of characters, their actions will appear less structured and repetitious, and more human in nature. This will also allow developers to easily create virtual characters with specific motivations, based mostly on their narrative purposes or roles in the virtual world. With minimum and maximum desirable motive values, the characters use reinforcement learning to drive action selection to maximize their rewards across all motives. Experimental results show that a character can learn to satisfy as many as four motives, even with significantly delayed rewards, and motive changes that are caused by other characters in the world. While the actions tested are simple in nature, they show the potential of a more complicated motivation driven reinforcement learning system. The developer need only define a character's motivations, and the character will learn to act realistically over time in the virtual environment.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".