arXiv:2606.15247v1 Announce Type: cross Abstract: The asymptotic behaviour of Monte Carlo Exploring Starts (MCES) is a long-standing open question in reinforcement learning, even in the tabular setting. We investigated the convergence properties of tabular MCES by constructing examples in which the algorithm converges to suboptimal solutions. This paper presents new counterexamples for both initial-visit and first-visit MCES and gives a convergence-restoring modification for the initial-visit case. We show that stable suboptimal solutions may exist for initial-visit MCES with sample-average up
This research addresses a long-standing theoretical problem in reinforcement learning, suggesting a foundational improvement to a common algorithm.
Improving the reliability and convergence properties of fundamental reinforcement learning algorithms is crucial for the development of more robust and trustworthy AI systems, particularly autonomous agents.
The understanding of Monte Carlo Exploring Starts (MCES) is refined, and a fix is proposed for some of its convergence issues, potentially leading to more stable and optimal AI learning policies.
- · AI researchers
- · Reinforcement learning developers
- · Developers of AI agents
- · AI systems relying on uncorrected MCES
Refined understanding and improved implementation of reinforcement learning algorithms.
More reliable training of AI agents, reducing the risk of suboptimal performance in deployed systems.
Accelerated development of complex autonomous AI, as foundational algorithms become more robust.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI