When Actions Need Nuance: Learning to Act Precisely Only When It Matters
Opening — Why this matters now Reinforcement learning has become impressively competent at two extremes: discrete games with neat action menus, and continuous control tasks where everything is a vector. Reality, inconveniently, lives in between. Most real systems demand choices and calibration—turn left and decide how much, brake and decide how hard. These are parameterized actions, and they quietly break many of today’s best RL algorithms. ...