n value iteration, or backward induction, the function π(s) is not utilized directly. Instead, its value is computed within V(s) when needed. The process involves a combined step where Vi+1(s)Vi+1(s) is determined by considering all possible actions aa and summing over possible future states s′s′ using the transition probabilities Pa(s,s′)Pa(s,s′), immediate rewards Ra(s,s′)Ra(s,s′), and the value of the next state Vi(s′)Vi(s′), discounted by a factor γ. The iteration begins with an initial guess for the value function and repeatedly updates the values until convergence to the Bellman equation. The concept of value iteration in the context of stochastic games was first presented in Lloyd Shapley's 1953 paper, but its importance was recognized later.

Princeton University

Spring 2019

This introductory course focuses on machine learning, probabilistic reasoning, and decision-making in uncertain environments. A blend of theory and practice, the course aims to answer how systems can learn from experience and manage real-world uncertainties.

No concepts data

+ 21 more conceptsStanford University

Autumn 2022-2023

Stanford's CS 221 course teaches foundational principles and practical implementation of AI systems. It covers machine learning, game playing, constraint satisfaction, graphical models, and logic. A rigorous course requiring solid foundational skills in programming, math, and probability.

No concepts data

+ 88 more conceptsCarnegie Mellon University

Spring 2018

A comprehensive exploration of machine learning theories and practical algorithms. Covers a broad spectrum of topics like decision tree learning, neural networks, statistical learning, and reinforcement learning. Encourages hands-on learning via programming assignments.

No concepts data

+ 55 more conceptsBrown University

Spring 2022

Brown University's Deep Learning course acquaints students with the transformative capabilities of deep neural networks in computer vision, NLP, and reinforcement learning. Using the TensorFlow framework, topics like CNNs, RNNs, deepfakes, and reinforcement learning are addressed, with an emphasis on ethical applications and potential societal impacts.

No concepts data

+ 40 more concepts