New research reveals flaws in Monte Carlo Exploring Starts for reinforcement learning

A new paper on arXiv introduces counterexamples to the convergence properties of Monte Carlo Exploring Starts (MCES) in reinforcement learning, demonstrating that it can converge to suboptimal solutions. The research highlights issues with both initial-visit and first-visit MCES, particularly concerning sample-average updates and the balance between exploration and exploitation. A proposed modification, which scales learning rates inversely to update frequencies on a state-by-state basis, is shown to guarantee convergence to optimality and is applicable to large-scale problems. AI

IMPACT Highlights critical dependencies between learning rates and update frequencies for convergence in reinforcement learning algorithms.

RANK_REASON The cluster contains a research paper published on arXiv detailing theoretical findings and proposed modifications to an algorithm in the field of reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• exploration
• first-visit MCES
• greedy actions
• initial-visit MCES
• Monte Carlo control
• Monte Carlo Exploring Starts
• non-greedy actions
• reinforcement learning
• sample-average updates

• paper
• other

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