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Assignment 5     

(This assignment is based on Problem 13.23 (edition 4) which you can find at the bottom. Do not do 13.23)

Jennifer was just born and her parents have decided to save money to pay for her college education.   They will invest $15,000 right now, and $10,000 each year on her future birthdays for the next 17 years (i.e. $10,000 when she turns 1, 2, 3,   …  16, 17).  They are bullish on America and are investing all of this money in the stock market.  The principal and returns are allowed to accumulate (i.e. are reinvested) in the equities market and no distribution of funds will be done before Jennifer starts college.  Assume that the returns of the stock market are normally distributed and are independent from year to year with a mean (µ) return of 7% and a standard deviation (σ) of 10%.  All investments returns are tax free.

REQUIRED  –  Jennifer is planning to go to college starting on her 18^th birthday; hence it is important for the parents to know how much money will have accumulated in her College Fund at that time (Call this the ACCUMULATION of the College Fund).  Construct a Monte-Carlo simulation model in EXCEL or CRYSTAL BALL (EXCEL add-on) to model the problem.  Run the simulation for at least 1,000 trials to answer the following questions:

1. What is the mean and the standard deviation of the ACCUMULATION?

1. What is the probability that the ACCUMULATION is over $300,000?  over $350,000? over $400,000?

1. What is the ACCUMULATION if the standard deviations is zero.  (Fixed return)

1. Can you compute a 95% confidence interval estimate of ACCUMULATION?    If so, what is it?  If not, why not?

1. Can this problem be solved without using simulation?  If so, how?

EXTRA CREDIT  –  Now consider the case where the rates of return are not independent from year to year.  There is an idea in finance that if returns are higher in a given year they will be lower in the next year.  (MEAN REVERSION).  Assume the following model:

The mean return in a given year  =  7% – α (Last year’s return –7%)

1. Modify your initial model and answer questions “a”, “b”, and “c” for  α = 0.25, 0.5, and 1.0

1. How do the results of this model compare with the original model?  Are they what you might have expected?  Comment.

COMP9444 Neural Networks and Deep Learning
Session 2, 2017
Project 3 – Deep Reinforcement Learning
Due: Sunday 29 October, 23:59 pm
Marks: 15% of final assessment

扫一扫又不会怀孕，扫一扫，作业无烦恼。
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Introduction
In this assignment we will implement a Deep Reinforcement Learning algorithm on some classic control tasks in the OpenAI AI-Gym Environment. Specifically, we will implement Q-Learning using a Neural Network as an approximator for the Q-function, with experience replay.
There are no constraints on the structure or complexity of network to be implemented, and marks are awarded on the basis of the learning speed and generality of your final model.
Because we are not operating on raw pixel values but already encoded state values, the training time for this assignment is relatively short, and each problem should only require approximately 15 minutes on a standard laptop PC.

The structure of this assignment is somewhat different to Assignment 1 and 2. Instead of functions to implement in a separate file, you have been given a partially completed python script.
Once you have implemented the unfinished parts of the script, you can run the code the same way you have with python3 neural_Qtrain.py. However, you can also call your main() function from another file, or invoke it from the command line if you wish to pass in specific argument values.
If running from the command line, navigate to the directory where the file is located and call the file like so:
$ python neural_Qtrain.py “CartPole-v0”
Would run the cartpole environment.
Alternatively, you can run the code interactively in ipython with e.g.
$ ipython
In [1]: import neural_Qtrain.py as qlearn
env, state_dim, action_dim, network_vars = qlearn.setup()
In [2]: qlearn.qtrain(env, state_dim, action_dim, *network_vars, render=True, num_episodes=5)
You can then run qlearn.qtrain() again with different values for “render”, “num_episodes”, etc. without reinitialising your network.

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扫一扫又不会怀孕，扫一扫，作业无烦恼。
留学顾问の QQ：2128789860
留学顾问の微信：risepaper

在保证论文质量的同时，我们尽可能的为同学们争取最低的价格，绝对让您感觉物有所值，更不会漫天要价。

Word limit: 2,000 words

CW2: 40% weighting: Learning outcome(s) to be assessed:   2

Working from a list of topic areas (see below) students to define their own essay title, agree this with their tutor and write a 2,000 word essay.

This individual assignment assesses your ability to:

Further develop understanding on a particular topic and critically evaluate the key issues. (Module Learning Outcome 2)

In order to achieve a high grade your work should be carefully researched, clearly argued, well presented and well structured. (Hand written work is unacceptable)

Topic Areas:

Strategic Management Accounting

Divisional Performance Measures

Pricing &Transfer Pricing

Environmental Management Accounting

Benchmarking

Business Ethics

Relevant Costing and Outsourcing

To help you choose, decide which topic interests you the most or the topic that relates to something you have done or are going to do.

Professional level of discussion of a publishable standard. Exceptional discussion with exceptional use of theory and critical evaluation.  Pertinent issues identified and discussed to exceptional depth, showing insight and independent thought.