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Lecture Notes in Computer Science
Reinforcement learning in real-world domains suffers from three curses of dimensionality: explosions in state and action spaces, and high stochasticity. We present approaches that mitigate each of these curses. To handle the state-space explosion, we introduce "tabular linear functions" that generalize tile-coding and linear value functions. Action space complexity is reduced by replacing complete joint action space search with a form of hill climbing. To deal with high stochasticity, wedoi:10.1007/11871842_74 fatcat:u4v7pizu2rabpfht2vgypgm2he