Adaptive Multi-Horizon Reinforcement Learning
Effective decision-making in complex and changing environments requires balancing short-term and long-term consequences. In reinforcement learning (RL), this trade-off is typically controlled through a fixed discount factor, which imposes a single exponentially discounted temporal horizon. However, biological agents exhibit flexible and adaptive temporal discounting, suggesting that effective planning requires multiple timescales. Here, we propose a multi-horizon approach that adaptively selects and combines temporal horizons, enabling robust adaptation to changes in reward structure without manual discount-factor tuning. This flexibility makes the method particularly suitable for continu...
| Authors | Manoosh Samiei, Doina Precup, Paul Masset |
|---|---|
| Primary category | cs.LG |
| Published | 22 July 2026 |
| Last updated at source | 28 July 2026 |
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