The Effectiveness of State Representation Model in Multi-Agent Proximal Policy Optimization for Multi-Agent Path Finding
Bibliographic record
Abstract
Multi-agent pathfinding plays a crucial role in various robot applications. Recently, deep reinforcement learning methods have been adopted to solve large-scale planning problems in a decentralized manner. Nonetheless, such approaches pose challenges such as non-stationarity and partial observability. In this paper, we address these challenges by integrating a state representation model into a multi-agent proximal policy optimization framework. To do so, we propose to utilize a state representation model which extracts representation features from the global map and leverages this information to enhance the training process. Our approach involves decoupling the feature extractor from the agent training process, enabling a more accurate representation of the global state that remains unbiased by the actions of the agents. Furthermore, our modularized approach offers the flexibility to replace the representation model with another model or modify tasks within the global map, without the retraining of the agents. We demonstrated the effectiveness of our approach by comparing three multi-agent proximal policy optimization frameworks. Our experimental results demonstrate that our approach improves the average episode reward compared to the other approaches.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".