Framework

A reinforcement learning agent that remains functional when reward signals are deceptive or adversarial.

A phase-aware RL architecture for environments with hidden regime shifts.

A transient operator that activates high-precision behavior only at critical moments, then disappears.

A stability-preserving RL variant designed to keep coherent behavior when the signal-to-noise ratio collapses.

An early-warning RL controller that tracks pre-critical instability and avoids collapse before it happens.

A minimal starting point: probes collapse interaction and establishes baseline stability measurements.