New Geometric Averaging Method Proposed for Stabilizing Linear Q-Learning Algorithms
Researchers have introduced a λ-target update mechanism that geometrically averages periodic hard target updates in linear Q-learning, a technique used to stabilize reinforcement learning algorithms. The method interpolates between one-period updates and projected Q-value iteration, with theoretical analysis provided through switching-system models. This work contributes to understanding how target update strategies can improve stability in Q-learning with linear function approximation.
A new preprint on arXiv proposes the λ-target update, a mechanism that applies geometric weighting to periodic hard target updates commonly used in deep Q-learning. The approach uses weights of the form (1-λ)λ^(m-1) where λ ranges from 0 to 1, creating a spectrum of update strategies: at λ=0 it recovers standard one-period updates, while as λ approaches 1 it converges to projected Q-value iteration. The authors analyze this mechanism specifically for linear Q-learning using switching-system models and related mathematical tools. While the paper focuses on a deterministic formulation for clarity, the authors indicate the approach extends to stochastic reinforcement-learning settings. The work addresses the broader problem of stabilization in Q-learning with function approximation, building on recent evidence that target updates improve algorithm stability.
What's missing
The paper does not discuss empirical validation results comparing the proposed λ-target update against existing stabilization methods on benchmark reinforcement learning tasks. Additionally, computational complexity analysis and practical guidance on selecting λ values for different problem domains are not addressed in the abstract.
What different sources said
- arXiv cs.LGCenter
Geometrically Averaged Hard Target Updates for Linear Q-Learning
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