Strange/README.md

# Phase Shift & Regime Uncertainty

## Strange / AntiStrange

This project studies reinforcement learning systems operating under hidden regime shifts and non-stationary dynamics. It focuses on how agents fail when the environment changes phase without explicit signaling, a scenario common in real-world deployment and safety-critical systems.

## Core Idea

Many environments appear stationary until they are not. This project explores:

- Latent phase transitions

- Sudden behavioral collapse despite stable metrics

- The gap between observed performance and underlying stability

Strange / AntiStrange are paired agents designed to probe these dynamics from different behavioral assumptions.

## Project Structure

- agents/

  #Strange and AntiStrange agent implementations.

- envs/

  #Phase-shifting environments, including dual\_hypothesis\_lab\_env.

- run\_all.py

  #Executes both agents in a shared environment for direct comparison.

- results/

  #CSV logs capturing regime transitions and performance degradation.

- graphics/

  #Comparative plots showing divergence under regime shifts.

## Running the Experiments

This project is designed to run without notebooks. (I resorted to Anaconda)

conda activate your\_env

python run\_all.py



\## Safety 

\## Why This Matters for AI Safety



-Hidden regime shifts are a major source of real-world AI failures. 

-This project operationalizes questions such as:



  -How long can agents operate under false assumptions?



  -What does “pre-collapse” behavior look like?



  -Can instability be detected before performance drops?



\## Related Work



-This project accompanies the preprint:

-Adaptive Stability Control in Sequential Learning Systems