New Geometric Averaging Method Proposed for Stabilizing Linear Q-Learning Algorithms
A new preprint introduces the λ-target update, a mechanism that geometrically averages periodic hard target updates in linear Q-learning to improve stability. The approach interpolates between standard one-period target updates and projected Q-value iteration depending on the parameter λ. The work offers a theoretical framework that could inform more stable training in deep reinforcement learning systems.
Researchers have proposed the λ-target update, a novel stabilization technique for Q-learning with linear function approximation. The method works by averaging m-periodic hard target update maps using geometric weights parameterized by λ ∈ [0,1], creating a continuous family of update rules. At λ=0, the method reduces to the standard one-period hard target update, while as λ approaches 1, it recovers projected Q-value iteration, effectively unifying two previously distinct approaches. The analysis employs a switching-system model and related mathematical tools to study convergence and stability properties. The paper currently treats a deterministic formulation, though the authors note the framework extends to stochastic reinforcement learning settings. Hard target updates are a widely used stabilization device in modern deep Q-learning, making theoretical insights into their behavior broadly relevant to the field.
What's missing
The paper is a preprint and has not yet undergone peer review. Key limitations include the restriction to a deterministic setting in the current analysis, with stochastic extensions left for future work. Empirical validation on standard deep reinforcement learning benchmarks is not reported, leaving the practical performance gains of the λ-target update relative to existing methods an open question.
What different sources said
- arXiv cs.LGCenter
Geometrically Averaged Hard Target Updates for Linear Q-Learning
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