Distributed Reinforcement Learning in Emergency Response Simulation
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents the implementation of a coordinated decision-making agent for emergency response scenarios. The agent’s implementation uses reinforcement learning (RL). RL is a machine learning technique that enables an agent to learn from experimenting. The agent’s learning is based on rewards, and feedback signals proportional to how good its actions are. The simulation platform used was infrastructure interdependencies simulator, in which, we have tested suitability of the approach in previous studies. In this paper, we have added new features to our previous solution, for enabling faster convergence and distributed processing. These additions include an enhanced reward scheme and a scheduler for orchestrating the distributed training. We include two test cases. The first case is a compact model with four critical infrastructures. In this model, the agent’s training required only 10% of the attempts needed in our previous version. Improvements in convergence come from adding a shaping reward scheme. We trained the agent across 24 simultaneous configurations of our model. The training process elapsed 4 min. The extended case included more infrastructures and a higher level of detail. The dimensionality of the problem grew by a factor of 4000, but the training converged in less episodes. We tested the extended model over 96 parallel instances (potential scenarios) with completion in 2.87 min. The results show a fast and stable convergence. This agent can help during multiple stages of emergency response including real-time situations.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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