Multi-Agent Formation Control With Obstacle Avoidance Using Proximal Policy Optimization
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, a formation of second-order holonomic agents is made to navigate through an obstacle field using proximal policy optimization (PPO) based deep reinforcement learning (DRL). The angle-based formation is allowed to shrink while maintaining its shape in order to navigate through tight spaces and take the geometric centroid of the formation towards the goal. Two reward schemes are presented, one based on the actions of individual agents and another based on the actions of the team as a whole. For each case, all the agents share a single policy that is trained in a centralized manner. Distance measurements, state information, error information regarding neighboring agents, and simulation information are used for training each policy in an end-to-end fashion. Simulation results for both approaches are compared.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 it