We reviewed Richard Bellman’s “A Markovian Decision Process” (1957), which introduced a mathematical framework for sequential decision-making under uncertainty.
By connecting recurrence relations to Markov processes, Bellman showed how current choices shape future outcomes and formalized the principle of optimality, laying the groundwork for dynamic programming and the Bellman equationThis paper is directly relevant to reinforcement learning and modern AI: it defines the structure of Markov Decision Processes (MDPs), which underpin algorithms like value iteration, policy iteration, and Q-learning.
From robotics to large-scale systems like AlphaGo, nearly all of RL traces back to the foundations Bellman set in 1957
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