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About this paper
Marcus Hutter, Samuel Yang-Zhao, Sultan J. Majeed
Researchers from The Australian National University established that temporal difference-like reinforcement learning algorithms with linear function approximation converge to the correct solution if and only if all non-zero linear combinations of chosen features possess "flat extrema", providing a unified theoretical explanation for previously observed non-uniqueness issues.
Key insights
- The core insight is that natural algorithms converge to the correct solution if and only if all non-zero linear combinations of features possess "flat extrema".
- This condition implies that for a majority of practical feature choices, natural algorithms are prone to non-uniqueness and convergence to incorrect solutions, even with perfect representability.
- State aggregation is a theoretically "safe" feature design choice, as it inherently produces features satisfying the flat extrema condition, guaranteeing convergence.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.