- NPTEL Design and analysis of algorithms Assignment 3 Answer 2023
- NPTEL Design and analysis of algorithms Assignment 2 Answer 2023
- NPTEL Cyber Security and Privacy Assignment 3 Answer 2023
- NPTEL Cyber Security and Privacy Assignment 2 Answer 2023
- NPTEL Cyber Security and Privacy Assignment 1 Answer 2023
NPTEL Reinforcement Learning Assignment 5 Answers 2022
We Discuss About That NPTEL Reinforcement Learning Assignment 5 Answers 2022
NPTEL NPTEL Reinforcement Learning Assignment 5 Answers 2022 – Here All The Questions and Answers Provided to Help All The Students and NPTEL Candidate as a Reference Purpose, It is Mandetory to Submit Your Weekly Assignment By Your Own Understand Level.
Are you looking for the Assignment Answers to NPTEL Reinforcement Learning Assignment 5 Answers 2022? If Yes You are in Our Great Place to Getting Your Solution, This Post Should be help you with the Assignment answer to the Nation al Programme on Technology Enhanced Learning ( NPTEL ) Course “NPTEL Reinforcement Learning Assignment 5 Answers 2022”
Table of Contents
NPTEL Reinforcement Learning
Next Week Assignment Answers
This course can have Associate in Nursing unproctored programming communication conjointly excluding the Proctored communication, please check announcement section for date and time. The programming communication can have a weightage of twenty fifth towards the ultimate score.
- Assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course.
- ( All assignments in a particular week will be counted towards final scoring – quizzes and programming assignments).
- Unproctored programming exam score = 25% of the average scores obtained as part of Unproctored programming exam – out of 100
- Proctored Exam score =50% of the proctored certification exam score out of 100
CHECK HERE OTHERS NPTEL ASSIGNMENTS ANSWERS
Below you can get your nptel reinforcement learning assignment 5 answers 2022 :.
In policy iteration, which of the following is/are true of the Policy Evaluation (PE) and Policy Improvement (PI) steps? The values of states that are returned by PE may fluctuate between high and low values as the algorithm runs.
PE returns the fixed point of Lπn
PI can randomly select any greedy policy for a given value function vn. Policy iteration always converges for a finite MDP. 1 point Consider Monte-Carlo approach for policy evaluation. Suppose the states are S1,S2,S3,S4,S5,S6 and terminal state. You sample one trajectory as follows – S1→S5→S4→S6→ terminal state. Which among the following states can be updated from this sample?
Ans – C 1 point Which of the following statements are true with regards to Monte Carlo value approximation methods? To evaluate a policy using these methods, a subset of trajectories in which all states are encountered at least once are enough to update all state-values. Monte-Carlo value function approximation methods need knowledge of the full model. Monte-Carlo methods update state-value estimates only at the end of an episode. All of the above.
Ans – D 1 point In every visit Monte Carlo methods, multiple samples for one state are obtained from a single trajectory. Which of the following is true? There is an increase in bias of the estimates. There is an increase in variance of the estimates. It does not affect the bias or variance of estimates. Both bias and variance of the estimates increase.
Ans – D 1 point Which of the following statements are FALSE about solving MDPs using dynamic programming? If the state space is large or computation power is limited, it is preferred to update only some states through random sampling or selecting states seen in trajectories. Knowledge of transition probabilities is not necessary for solving MDPs using dynamic programming. Methods that update only a subset of states at a time guarantee performance equal to or better than classic DP. None of the above.
Ans – B 1 point Select the correct statements about Generalized Policy Iteration (GPI). GPI lets policy evaluation and policy improvement interact with each other regardless of the details of the two processes. Before convergence, the policy evaluation step will usually cause the policy to no longer be greedy with respect to the updated value function. GPI converges only when a policy has been found which is greedy with respect to its own value function. The policy and value function found by GPI at convergence with both be optimal.
Ans – C 1 point What is meant by ”off-policy” Monte Carlo value function evaluation? The policy being evaluated is the same as the policy used to generate samples. The policy being evaluated is different from the policy used to generate samples. The policy being learnt is different from the policy used to generate samples. The policy being learnt is different from the policy used to generate samples.
Ans – A 1 point For both value and policy iteration algorithms we will get a sequence of vectors after some iterations, say v_1, v_2….v_n for value iteration and v’1,v’2…v’n for policy iteration. Which of the following statements are true.
For all vi∈v1,v2….vn there exists a policy for which vi is a fixed point.
For all v’i∈v’1,v’2….v’n there exists a policy for which v’i is a fixed point.
For all vi∈v1,v2….vn there may not exist a policy for which v_i is a fixed point.
For all v’i∈v’1,v’2….v’n there may not exist a policy for which v’i is a fixed point.
Ans – B 1 point Given that L is a contraction in Banach space, which of the following is true?
L must be a linear transformation.
L has a unique fixed point.
Ans – C 1 point Which of the following are true? The bellman optimality equation defines a contraction in Banach space. The bellman optimality equation can be re-written as a linear transformation on the value function vector v, where each element of v corresponds to the value of a state of the MDP.
The final value estimates obtained at the stopping condition of value iteration will be optimal values, v∗
The final policy obtained by greedily selecting actions according to the returned value function v at the stopping condition of value iteration will be an optimal policy
- Team of Two CodeChef Solution
- NPTEL Social Network Analysis Assignment 5 Answers 2022
You May Also Like
Nptel public speaking assignment 7 answers 2022, nptel public speaking assignment 4 answers 2022, nptel public speaking assignment 5 answers 2022.
CS234: Reinforcement Learning Winter 2023
- The poster session will be from 11:30am-2:30pm in the Huang Foyer (area outside of NVIDIA auditorium).
Course Description & Logistics
- Lectures will be live every Tuesday and Thursday: Videos of the lecture content will also be made available to enrolled students through canvas.
- Lecture Materials (videos and slides) : All standard lecture materials will be delivered through modules with pre-recorded course videos that you can watch at your own time. Each week's modules are listed in the schedule and can be accessed here , and will be posted by the end of Sunday before that week's class. Guest lectures will be presented live and recorded for later watching. Recordings will be available to enrolled students through Canvas.
- 1:1 office hours: Students can sign up for 1:1 office hours with faculty and CAs. These will all be appointment-based so that students need not to wait in queue. See our calendar for times and sign up links. [Office hour schedules will be posted by the end of Tuesday on week 1] --> here . These may be offered in person but will definitely be offered via zoom. -->
- Problem session practice: We will also make available optional additional problem session questions and videos to provide additional opportunities to learn about the material.
- Quizzes: Instead of a large high-stakes midterm, there will be four quizzes over the course. We will drop the lowest score of Quiz 1-3.
- Project: There will be no final project.
- --> Platforms: All assignments and quizzes will be handled through Gradescope, where you will also find your grades. We will send out links and access codes to enrolled students through Canvas. You can find previous years ( Winter 2022 , Winter 2021 , Winter 2020 , Winter 2019 ) materials here.