Python代写|机器学习代写 - Advanced Machine Learning Assignment
Advanced Machine Learning Assignment — 2020 Submission Please submit your solution electronically via vUWS. Submit a report as PDF and your code zipped into one file, and please include the signed and completed cover sheet that you can find at the end of the document. Submission is due on 15 Oct 2020, 11:59pm. Minipong Figure 1: 4 frames from our data, using +1 valued pixels for +, , 1 for the paddle. In this assignment we work with data and a simulation of a simple version of “pong”. Two objects appear on the field: a + object as “ball”, and a paddle that can take different spots, but only in the bottom row. Pixels of the two objects are represented with different the values s 1 and 1 while background pixels have the value 0. The two markers at the top corners are fixed (( 1 and +1, respectively) and appear in every frame. Preparation Download the and python files. The class implements the pong game simulation. Running will create datasets of pong screenshots for your first task. A new pong game can be created like here: from minipong import Minipong pong = Minipong(level=1, size=5) In this, level sets the information a RL agent gets from the environment, and size sets the size of the game (in number of different paddle positions). Both paddle and + are 3 pixels wide, and cannot leave the field. A game of size 5 is (15×15) pixels, and the ball x- and y-coordinates can be values between 1 and 13. The paddle can be in 5 different locations (from 0 to 4). Task 1: Train a CNN to predict object positions 15 points The python program creates a training and test set of “minipong” scenes, trainingpix.csv (676 samples) and testingpix.csv (169 samples). Each row represents a 15×15 screenshot (flattened in row-major order). Labels appear in sep￾arate files, traininglabels.csv and testlabels.csv. They contain 3 labels for each example (x/y/z), the x/y-coordinates for the + marker with values between 1 and 13, and z between 0...4, for the location of the paddle. Steps 1. Create the datasets by running the code. 2. Create a CNN that predicts the x-coordinate of the + marker. • You can (but don’t have to) use an architecture similar to what we used for classifying MNIST, but be aware the input dimensions and outputs are different, so you will have to make at least some changes. • You can normalise/standardise the data if it helps improve the training. 3. Create a CNN that predicts all three outputs (x/y/z) from each input1. • Compute the accuracy on the test data set. What to submit: • Submit the python code of your solutions (two versions). • For your report, write a brief description of your steps to create the models and your prediction. What did you do? Please also include answers to the following questions: – What loss did you use, why? What is your loss for the second model? – For how long did you train your model (number of epochs, time taken)? What is the performance on the test set? • For all solutions: the way you try to solve tasks and your description is more important that absolute performance of your code. If things do not work as you hope, submit your steps and describe what the specific problem is. 1As an intermediate step, consider 3 separate networks, one for each output. Then try merge these into one network with 3 outputs. 2 Task 2: Train a convolutional autoencoder 10 points Instead of predicting positions, create a convolutional autoencoder that compresses the pong screenshots to a small number of bytes (the encoder), and transforms them back to original (in the decoder part). Steps 1. Create and train an (undercomplete) convolutional autoencoder and train it using the training data set from the first task. 2. You can choose the architecture of the network and size of the representation h = f(x). The goal is to learn a representation that is smaller than the original, and still leads to recognisable reconstructions of the original. 3. For the encoder you can use the same architecture that you used for the first task, but you cannot use the labels for training. You can also create a completely dif￾ferent architecture. 4. (No programming): In theory, what would be the absolute minimal size of the hidden layer representation that allows perfect reconstruction of the original im￾age? What to submit: • Submit the python code of your solution. • For your report, write a brief description of your steps to create the models and your prediction. What did you do (e.g., what loss function, how big is the encoded image in your architecture, how many steps did the learning take)? • Include screenshots of 1-2 output images next to the original inputs (e.g., select a good and a bad example). Task 3: Create a RL agent for Minipong (level 1) 15 points The code in provides an environment to create an agent that can be trained with reinforcement learning (a complete description at the end of this sheet). It uses the objects as described above. The following is a description of the environment dynamics: 3 • The + marker moves a diagonal step at each step of the environment. When it hits the paddle or a wall (on the top, left, or right) it reflects. • The agent can control the paddle ( ), by moving it one 3-pixel slot every step. The agent has three actions available: it can choose to do nothing, or it can move it to the left or right. The paddle cannot be moved outside the boundaries. • The agent will receive a positive reward when the + reflects from the paddle. In this case, the + may also move by 1 or 2 random pixels to the left or right. • An episode is finished when + reaches the bottom row without reflecting from the paddle. In a level 1 version of the game, the observed state (the information made available to the agent after each step) consists of one number: dz. It is the relative position of the +, relative to the centre of the paddle: a negative number if + is on one side, a positive one on the other. For this task, you can initialise pong like this: pong = Minipong(level=1, size=5) or like this: pong = Minipong(level=1, size=5, normalise = False) In the first version, step() returns normalised values of dz (values between n 1...1) for the state, while in the second version it returns pixel differences (( 13...13). Steps 1. Manually create a policy (no RL) that successfully plays pong, just selecting ac￾tions based on the state information. The code contains a tem￾plate that you can use and modify. 2. Create a (tabular or deep) TD agent that learns to play pong. For choosing actions with -greedy action selection, set  = 1, initially, and reduce it during your training to a minimum of 0.1. 3. Run your training, resetting after every episode. Store the sum of rewards. After or during the training, plot the total sum of rewards per episode. This plot — the Training Reward plot — indicates the extent to which your agent is learning to improve his cumulative reward. It is your decision when to stop training. It is not required to submit a perfectly performing agent, but show how it learns. 4 4. After you decide the training to be completed, run 50 test episodes using your trained policy, but with  = 0.0 for all 50 episodes. Again, reset the environment at the beginning of each episode. Calculate the average over sum-of-rewards-per￾episode (call this the Test-Average), and the standard deviation (the Test-Standard￾Deviation). These values indicate how your trained agent performs. 5. If you had initialised pong with pong = Minipong(level=2, size=5), the observed state would consist of 2 values: the ball y-coordinate, and the relative +- position dz from level 1. Will this additional information help or hurt the learning? (No programming required). What to submit: • Submit the python code of your solutions (both the manual strategy, and the code of your RL learner). • For your report, describe the solution, mention the Test-Average and Test-Standard￾Deviation, and include the Training Reward plot described above. After how many episodes did you decide to stop training, and how long did it take? • Please don’t forget to include the answer about the level 2 version question. Task 4: Create a RL agent for Minipong (level 3) 10 points In a level 3 version of the game, the observed state (the information made available to the agent after each step) consists of three number: y, dx, dz. These are y, the ball y￾coordinate; dx, the change in ball x-coordinate from last step to now; and dz (same as previous levels). For this task, you can initialise pong in two ways: pong = Minipong(level=3, size=5) pong = Minipong(level=3, size=5, normalise = False) In the first version, step() returns normalised values of y and dz (values between 粆 1...1), while in the second version these values are unnormalised. The dx values are always unnormalised (but should be -1 or 1 in most cases, except after the paddle has been hit). Steps 1. Create a (neural-network based) RL agent that finds a policy using (all) level 3 state information. Use a discount factor γ = 0.95. 5 2. You can choose the algorithm (deep TD or deep policy gradient). 3. Try to train an agent that achieves a running reward > 300 (the file has an example for how to calculate this). 4. Don’t go overboard with the number of hidden layers as this will significantly increase training time. Try one hidden layer. 5. Write a description explaining how your approach works, and how it performs. If some (or all) of your attempts are unsuccessful, also describe some of the things that did not work, and which changes made a difference. What to submit: • Submit the python code of your solutions. • For your report, describe the solution, mention the Test-Average and Test-Standard￾Deviation, and include the Training Reward plot described above. Tips 1. For the RL-tasks, it often takes some time until the learning picks up, but they should not take hours. If the agent doesn’t learn, explore different learning rates. For Adam, try values between 5e-3 (faster) and 1e-4 (slower). 2. Even if the learning does not work, remember that we would like to see that you understood the ideas behind the code. So describe the ideas that you tried, and still submit your code but say what the problem was. Bonus questions I can do it Neural Networks: Train a neural network that predicts the dz variable. Bring it on Pong level 3: Modify the learning or the reward from the environment so the agent avoids moving the paddle unnecessarily. Compare the learned policies. Hardcore Train an autoencoder where you can use the encoded image as input to an RL agent that successfully plays pong. Nightmare Solve Minipong(level = 0) in pytorch. In this level, the state is a difference image (pixels) between the current state and the previous state. Check for tips. 6 If you put the file into your working directory, you can import the class like this: from minipong import Minipong big = 7 pong = Minipong (level =1, size=big) The Minipong class has several functions that you will have to use. The file contains an example and an explanation for many of the functions (check it out), but here is a brief list: pong = Minipong (level=level , size=size) n = pong. observationspace () state = pong.state () state = pong. transition (action) done = pong.terminal () r = pong.reward () state , r, done = pong.step(action) state = pong.reset () action = pong. sampleaction () pong.render(text = False , reward=r) pix = pong. to pix (pong.s1) You can ask or answer questions about how to use the files provided with this assign￾ment on discord, as long as they are general python / programming questions, for exam￾ple if the code provided does not work for you as expected. You must not ask or answer questions to the machine learning questions in this assignment anywhere, including dis￾cord. If in doubt, ask your friendly lecturers or tutor first. 7 Assignment Cover Sheet School of Computer, Data, and Mathematical Sciences Student Name Student Number Unit Name and Number 301119: Advanced Machine Learning Title of Assignment Assignment 1 Due Date 15 Oct 2020 Date Submitted DECLARATION I hold a copy of this assignment that I can produce if the original is lost or damaged. I hereby certify that no part of this assignment/product has been copied from any other students work or from any other source except where due acknowledgement is made in the assignment. No part of this assignment/product has been written/pro￾duced for me by another person except where such collaboration has been authorised by the subject lecturer/tutor concerned. Signature: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (Note: An examiner or lecturer/tutor has the right not to mark this assignment if the above declaration has not been signed) Task 1 Task 2 Task 3 Task 4 Bonus Total Mark Possible 15 10 15 10 ? 50 The maximum points possible for this assignment is 50 (including any bonus points). 8