SOBRE ESTE EPISÓDIO
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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OUTROS EPISÓDIOS NESTE PODCAST
In this episode, we review Cauchy’s 1847 paper, which introduced an iterative method for solving simultaneous equations by minimizing a function using its partial derivatives. Instead of elimination, he proposed progressively reducing the function’s value through small updates, forming an early ver…
In the 1st episode of the second season we review the legendary Marvin Minsky's "Steps Toward Artificial Intelligence" from 1961.
Itis a foundational work in the field of AI that outlines the challenges and methodologies for developing intelligent problem-solving systems. The paper categorizes AI …
In the 23rd episode we review the The 1953 paper Metropolis, Nicholas, et al. "Equation of state calculations by fast computing machines."
The journal of chemical physics 21.6 (1953): 1087-1092 which introduced the Monte Carlo method for simulating molecular systems, particularly focusing on two-…
We review Richard Bellman's "The Theory of Dynamic Programming" paper from 1954 which revolutionized how we approach complex decision-making problems through two key innovations. First, his Principle of Optimality established that optimal solutions have a recursive structure - each sub-decision mus…
At the 24th episode we go over the paper titled:Dempster, Arthur P., Nan M. Laird, and Donald B. Rubin. "Maximum likelihood from incomplete data via the EM algorithm." Journal of the royal statistical society: series B (methodological) 39.1 (1977): 1-22.The Expectation-Maximization (EM) algorithm i…
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