New Long-Horizon Q-Learning method improves reinforcement learning accuracy

Researchers have introduced Long-Horizon Q-Learning (LQL), a novel method designed to improve the stability of value-based reinforcement learning. LQL addresses the issue of compounding estimation errors in traditional Q-learning by incorporating an inequality-based backstop. This approach penalizes violations of bounds derived from observed action sequences, using existing network outputs to avoid additional computational overhead. Experiments show LQL consistently outperforms standard 1-step and n-step TD learning across various benchmarks. AI

IMPACT Introduces a method to stabilize reinforcement learning, potentially improving agent performance in complex, long-horizon tasks.

RANK_REASON This is a research paper detailing a new algorithm for reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Long-Horizon Q-Learning
• n-step TD learning
• Q-learning
• reinforcement learning
• TD updates

• paper
• other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →