Context Detection and Identification In Multi-Agent Reinforcement Learning With Non-Stationary Environment
Why this work is in the frame
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Bibliographic record
Abstract
Reinforcement learning methods are mostly constructed on the very assumption that environments are stationary. However, most real world environments are non-stationary; that is, we assume they are composed of several stationary components (i.e., sub-environments or contexts). So, methods with this assumption are not capable of learning non-stationary environments. Reinforcement Learning - Context Detection (RL-CD) method enables the agent to learn the environment without prior information; detect the environment’s context change points and create a partial model for each context. The underlying environment of this approach is single-agent and has shortcomings for multi-agent learning. In this study, we introduce a new approach called Multi-agent reinforcement learning-context detection (MARL-CD), which can both detect context change points and enable agents to learn non-stationary environments with multi-agent settings. This approach is based on RL-CD approach. MARL-CD is more efficient in terms of detecting context change created by the agents on the environment and detecting the context change of the environment itself. It enables an agent to detect the context changes not only from the change of environment dynamics but also from policy changes of agents in the environment. In the approach in this study, it has been shown by the experimental results that the agents spend 16% less energy and are more efficient than RL-CD in terms of detecting the change points more accurately.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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