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Reinforcement Learning Part 2/2 (CS 188)

Pieter Abbeel UC Berkeley

Professor

1:25:00·2018·undergraduate level

This lecture explores advanced Reinforcement Learning topics including the exploration-exploitation dilemma with Epsilon Greedy policies, approximate Q-learning using feature-based representations, and an introduction to policy search.

AI & ML Reinforcement Learning Systems Security Exploration-Exploitation Approximate Q-Learning

COMPSCI 188, LEC 001 - Fall 2018

COMPSCI 188, LEC 001 - Pieter Abbeel, Daniel Klein

Copyright @2018 UC Regents; all rights reserved

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Reinforcement Learning Part 1/2 (CS 188)

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Probability (CS 188)

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Part of course22 lectures

CS 188: Introduction to Artificial Intelligence

Prof. Pieter Abbeel

Uninformed and A* heuristic search, constraint satisfaction, adversarial minimax/expectimax games, Markov decision processes, reinforcement learning, Bayes nets, and particle filtering.

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