Uprising E-sports Industry: machine learning/AI improve in-game performance using deep reinforcement learning
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
With the quick development of machine learning, deep reinforcement learning will have a big influence on E-sports. It can be considered machine learning will help us train E-sports players easily and effectively and give coaches and players some new ideas to train and win the games. Flappy Bird is a game where the players try to keep the bird alive as long as possible and get a high score. Flappy Bird is a good example to prove that the thought is feasible. In this project, a flappy bird training AI is developed based on Q-learning and DQN. The game is played by the models obtained from deep reinforcement learning, and the game is also played by humans. Then get experimental data from these two ways and compare them. For the two ways of playing the game (by AI or manually), there are many similarities in the increased rate of scores as training sessions increase, which means AI can “teach” players how to train to get a higher score. It can be applied to skills and experience-based games and help us to train top players. Maybe it can also be applied to other fields, such as helping engineers escape from potential errors and accidents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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