Adaptive Multi-Horizon RL：狀態依賴閘道與固定 γ 的工程取捨
研究團隊提出狀態依賴閘道網路，讓智能體在持續學習中自動權重不同時間視角。透過 Expected SARSA 與 TD error 優化，成功在 MiniGrid 任務間切換時保持穩定回報（36–40），降低手動選擇單一折現率的工程負擔。此方法高度依賴狀態特徵的資訊密度以區分時間結構。
Temporal Horizon
Adaptive Multi-Horizon RL: Engineering Tradeoffs of State-Dependent Gating and Fixed γ
A research team proposes a state-dependent gating network that lets an agent automatically weight different temporal perspectives during continual learning. Using Expected SARSA and TD-error optimization, the architecture maintained stable episode returns of 36–40 while switching between MiniGrid tasks, reducing the engineering overhead of manually choosing one discount factor. The key constraint is state-feature information density: the system must distinguish temporal structures.