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The agent receives a reward of 100 if it hits the top of the slope and -1 otherwise. The actions available are forward (action 0), neutral (1), and reverse (2). The observations available to the agent are the position of the car measured along the slope, the velocity of the car and the acceleration of the car – all real numbers. s part of a reinforcement learning solution to this problem: 1. Write a linear value approximation function (i.e., non-tabular value) for this problem and clearly show how the set of weights are used

The agent receives a reward of 100 if it hits the top of the slope and -1 otherwise. The actions available are forward (action 0), neutral (1), and reverse (2). The observations available to the agent are the position of the car measured along the slope, the velocity of the car and the acceleration of the car – all real numbers. s part of a reinforcement learning solution to this problem: 1. Write a linear value approximation function (i.e., non-tabular value) for this problem and clearly show how the set of weights are used

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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The agent receives a reward of 100 if it hits the top of the slope and -1 otherwise. The actions available are forward (action 0), neutral (1), and reverse (2). The observations available to the agent are the position of the car measured along the slope, the velocity of the car and the acceleration of the car all real numbers. As part of a reinforcement learning solution to this problem: 1. Write a linear value approximation function (i.e., non-tabular value) for this problem and clearly show how the set of weights are used.
Transcribed Image Text:The agent receives a reward of 100 if it hits the top of the slope and -1 otherwise. The actions available are forward (action 0), neutral (1), and reverse (2). The observations available to the agent are the position of the car measured along the slope, the velocity of the car and the acceleration of the car all real numbers. As part of a reinforcement learning solution to this problem: 1. Write a linear value approximation function (i.e., non-tabular value) for this problem and clearly show how the set of weights are used.
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