Several Reinforcement Learning Methods in Mean-Field Games with Binary Action Spaces
Bibliographic record
Abstract
Recent years have witnessed significant progress in the sub-field of machine learning known as reinforcement learning, in which interactions between intelligent agents and the environment enable agents to learn and solve sequential decision-making problems through accumulating rewards with delays.Despite much success in single-player settings, reinforcement learning in multi-agent domains remains a challenging task in many aspects.In this thesis, the mean-field approach will be used to study binary action space stochastic games with a sufficiently large number of players that can be generalized to the multi-population case.Based on the mean-field approximation, several algorithms will be implemented and compared in numerical experiments to visualize their convergence to the equilibrium policy.It is my greatest honor to dedicate this thesis to my supervisor, Minyi Huang, without whom by no means could I finish this thesis.It was Professor Huang who introduced me to the mean-field games theory; it was Professor Huang who academically guided me and financially supported me; it was Professor Huang who cheered me up when I was deeply frustrated by the slow progress of my work.During the pandemic, face-to-face communication was unavailable, but Professor Huang strived to maintain a routine video communication with me.Moreover, Professor Huang invited me to a variety of virtual academic conferences, which facilitated me to grasp a deeper understanding of the meanfield game theory and reinforcement learning.I would like to also dedicate my thesis to my parents.Due to Covid-19, I had to spend another academic year to complete my thesis, and my parents were still willing to support me, both financially and emotionally.My family originally comes from Wuhan, where the first outbreak of Covid-19 was observed, so last year was a tough year for my entire family.I hope this thesis marks a new beginning for me and my family.
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.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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 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".