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A curated collection of counterexamples that demonstrate limitations and pitfalls in type systems, serving as an educational resource for programmers and language designers.
This paper presents counterexamples showing that Monte Carlo Exploring Starts can converge to suboptimal solutions in tabular reinforcement learning, and provides a modification that guarantees convergence to optimality by scaling learning rates inversely to update frequencies.