On the Equilibrium Between Feasible Zone and Uncertain Model in Safe Exploration

This research addresses how to safely explore environments using reinforcement learning (RL, a type of AI training where a system learns by trial and error) without causing damage or violating safety rules. The paper introduces safe equilibrium exploration (SEE), a method that balances two competing goals: expanding the area where exploration is allowed (the feasible zone) and building a more accurate model of how the environment works, showing that these two objectives improve each other and can reach an optimal balance without any safety violations.