Consider Slide 55 in "8 Reinforcement Learning.ppt". We have a part of another trajectory: (c1,E, -1) -> (c2,N, -1).。 Under SARSA, the new Q(c1,E) =• Under Q-Learning, the new Q(c1,E) =answer)Picture below is of the slideN(s,a)(keep one decimal value in your answer)(keep one decimal value in yourQ(s, a)631-2.4-1.841.010105512 02525+100C-2.416.4-1.860.8 0.088.8+1001131-2.4-1.83.4185139-2.945.8955552 -100b-2.9-2.912.2 -101.0-1004-2.9-51.564284-2.6-1.911.0-101.000817 320 1888a-2.5-2.2-2.00.5 -2.0-2.1 -15.0 -14.964477-2.4-2.1-1.9-3.712341234

To ensure the correctness of the solution, let's carefully re-evaluate the calculations…

Answered: Consider Slide 55 in "8 Reinforcement…

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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A unigram is a sequence of words of length one (i.e. a single word).• A bigram is a sequence of words of length two.• The conditional probability of an event E2 given another event E1, written p(E2|E1), is the probability that E2 will occur given that event E1 has already occurred.We write p(w(k)|w(k-1)) for the conditional probability of a word w in position k, w(k), given the immediately preceding word, w(k-1). You determine the conditional probabilities by determining unigram counts (the number of times each word appears, written c(w(k)), bigram counts (the number of times each pair of words appears, written c(w(k-1) w(k)), and then dividing each bigram count by the unigram count of the first word in the bigram:p(WORD(k)|WORD(k-1)) = c(WORD(k-1) WORD(k)) / c(WORD(k-1)) Apply and incorporate instrutions to code below. #include <stdio.h>//including headers#include <string.h>#include <stdlib.h> struct node{//structure intialization int data; struct node *next; };…
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pls help fast will like
Sometimes, like with 203, the sequence reaches (and stays at) 1. Numbers like this are called happy. Not all numbers are happy. If we started with 11, the sequence would be: 11 -> 2 -> 4 -> 16 -> ... This sequence will never reach 1, and so the number 11 is called unhappy. Given a positive whole number, you have to determine whether that number is happy or unhappy. Examples happy (203) → true happy (11) → false happy (107) → false Notes
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Describe a Python program of ensemble methods by doing the following: If each base-learner is valid and correct with probability p > 1 / 2, what is the probability that a majority vote over L classifiers gives the correct answer?
A propositional knowledge-base KB consisting of five sentences is given below (note that "/\" is used to denote "logical and", and "\/" is used to denote "logical or", and "~" is used to denote "logical negation"): 1. (~P /\ ~Q ) -> R2. R -> S3. ~S4. P -> ~U5. U a) Explain in English (in Steps!) that KB |= Q. b) Show resolution steps that leads to KB |- Q. You should first convert the sentences in the KB into clauses.
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Please formulate the hypothesis space bias ratio of Inductive Learning; use i: number of inputs, v: number of different values of one input.(assume that all the inputs have same number of different values)

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