Adaptive RL Balances Short and Long-Term Decisions

In reinforcement learning (RL), decision-making often involves balancing immediate rewards against future outcomes, typically managed by a fixed discount factor. However, this single-horizon approach can be inflexible, especially in dynamic environments where the optimal balance shifts. Researchers have developed a multi-horizon RL method that adaptively chooses and blends different temporal horizons. This eliminates the need for manual tuning of discount factors, enabling the system to robustly adapt to changes in reward structures and environmental configurations. The approach is particularly effective in continual learning settings, where tasks or environments change sequentially. Empirical tests in MiniGrid environments demonstrated its ability to identify effective discount factors and improve parameter efficiency and adaptability in both artificial and biologically inspired learning systems.