Scalar-Stepsize Nonuniform Monte Carlo Optimistic Policy Iteration: A Certified Counterexample
arXiv:2606.15978v1 Announce Type: new Abstract: Tsitsiklis proved convergence of Monte Carlo optimistic policy iteration under a uniform update structure and identified nonuniform update frequencies as a delicate obstruction. We give a certified negative answer for the natural scalar-stepsize, unnormalized asynchronous state-value recursion with fixed nonuniform state-selection probabilities. In a three-state, two-action discounted MDP, the nonuniform update frequencies induce a diagonally scaled greedy-policy mean field with a certified nonconstant attracting hybrid periodic orbit. With a bou
This academic paper presents a theoretical counterexample in a specific area of Monte Carlo optimistic policy iteration.
For a sophisticated reader, this represents a niche but important theoretical development within deep reinforcement learning research.
It refines understanding of convergence conditions in Monte Carlo policy iteration, specifically under nonuniform update frequencies.
- · AI researchers (theoretical)
- · Deep reinforcement learning (DRL) community
Refines theoretical understanding of DRL algorithm limitations.
Potentially informs the design of more robust DRL algorithms in the future.
Indirectly contributes to the long-term progress of AI agent development by addressing fundamental issues.
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Read at arXiv cs.LG