Multi-Head DDPG for Pursuit-Evasion with Interpretable Behavioral Decomposition
Why this work is in the frame
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Bibliographic record
Abstract
Designing scalable and interpretable control strategies for decentralized multi-agent systems remains a challenge in reinforcement learning (RL).This challenge is particularly evident in pursuit-evasion tasks, which require coordination under partial observability, without explicit communication or centralized guidance.Although deep RL methods achieve strong performance, they typically operate as black boxes, limiting trust and deployment in safety-critical domains.We propose a Multi-Head DDPG architecture that decomposes control into three interpretable force components -pursuit, cohesion, and separation -weighted adaptively to generate context-aware actions.This design enables emergent role differentiation and interpretable self-organization in the model.In grid-based pursuit-evasion benchmarks, our method outperforms DQN, PPO, and standard DDPG in terms of success rate, convergence speed, and generalization, while also yielding transparent collective behaviors.Overall, the results show that weighted force-based behavioral decomposition provides a principled pathway toward achieving both highperformance and explainable multi-agent control.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.000 | 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