Convergence of Monte Carlo Optimistic Policy Iteration: Beyond Uniform State-Action Updates

arXiv:2606.10580v1 Announce Type: new Abstract: The asymptotic behaviour of Monte Carlo optimistic policy iteration (MC-O-PI) is a long-standing open question. When the model of the environment is unknown, as is common in practice, the only known condition that guarantees convergence to optimality is impractical. In its canonical form, this condition requires that the episodes used for policy evaluation be initialised uniformly over the entire state-action space. This paper strictly relaxes that requirement. Specifically, we prove that initial-visit MC-O-PI converges to optimality even when up

Source: arXiv cs.LG — read the full report at the original publisher.

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