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ccba775 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 | \# Model Description: Strange / AntiStrange
\## Purpose
Strange / AntiStrange are reinforcement learning agents designed to study failure under hidden regime shifts and phase transitions. The focus is on internal instability rather than external performance.
\## Strange Agent
\*\*Role:\*\*
Strange represents a standard learner assuming environmental stationarity.
\*\*Key Properties:\*\*
\- Performs well under initial regimes
\- Fails abruptly when latent dynamics shift
\- Continues acting confidently despite invalid assumptions
\## AntiStrange Agent
\*\*Role:\*\*
AntiStrange is designed as a counterfactual probe with altered sensitivity to regime inconsistency.
\*\*Key Properties:\*\*
\- Detects instability earlier
\- Deviates sooner from established policies
\- Enables comparative analysis of collapse timing
\## Dual Hypothesis Environment
The `dual\_hypothesis\_lab\_env` exposes both agents to identical observations while enforcing hidden regime changes. This enables direct comparison of:
\- Adaptation lag
\- Behavioral divergence
\- Collapse signatures
\## Safety
These models formalize a critical safety risk:
> Systems that fail not because of noise, but because the world quietly changes.
They are intended as diagnostic tools for studying early-warning signals and regime-aware control strategies.
\## Notes
The implementation prioritizes clarity and inspectability over complexity to support safety-focused analysis and experimentation.
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