Timothy Tomashevskiy

Adjustment Speed as a Safety Constraint for Nonstationary Reinforcement Learning

The proposed framework uses learned context representations and short-horizon context forecasts to estimate adaptation demand and compare it with the agent's achievable adaptation capacity, and proactively tightens the admissible action set and activates an action-level shield to reduce unsafe behavior before violation...