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Assignment: Reinforcement Learning and Deep Learning
Contents
Part 1: Q-learning (Snake)
Provided Snake Environment
Q-learning Agent
Debug Convenience
Part 2: Deep Learning (MNIST Fashion)
Background
Neural Network
Implementation Details
Testing
What to Report
Warning/Hints
Deliverables
Report Checklist
Part 1: Q-learning (Snake)
Snake is a famous video game originated in the 1976 arcade game Blockade. The player uses up, down, left and right to
control the snake which grows in length(when it eats the food), with the snake body and walls around the environment being
the primary obstacle. In this assignment, you will train AI agents using reinforcement learning to play a simple version of
the game snake. You will implement a TD version of the Q-learning algorithm.
Image from Wikipedia
Provided Snake Environment
Snake
In this assignment, the size of the entire game board is 560x560. The green rectangle is the snake agent and the red rectangle
is the food. Snake head is marked with a thicker boarder for easier recognition. Food is generated randomly on board once
the initial food is eaten. The size for every side of wall (filled with blue) is 40. The snake head, body segment and food have
the same size of 40x40. Snake moves with a speed of 40 per frame. In this setup, the entire board that our snake agent can
move has a size of 480x480 and can be treated as a 12x12 grid. Every time it eats a food, the points increases 1 and its body
grows one segment. Before implementing the Q-learning algorithm, we must first define Snake as a Markov Decision
Process (MDP). Note in Q-learning, state variables do not need to represent the whole board, it only needs to
represent enough information to let the agent make decisions.(So once you get environment state, you need to convert
it to the state space as defined below). Also, the smaller the state space, the more quickly the agent will be able to
explore it all.
State: A tuple (adjoining_wall_x, adjoining_wall_y, food_dir_x, food_dir_y, adjoining_body_top,
adjoining_body_bottom, adjoining_body_left, adjoining_body_right).
[adjoining_wall_x, adjoining_wall_y] gives whether there is wall next to snake head. It has 9 states:
adjoining_wall_x: 0 (no adjoining wall on x axis), 1 (wall on snake head left), 2 (wall on snake head right)
adjoining_wall_y: 0 (no adjoining wall on y axis), 1 (wall on snake head top), 2 (wall on snake head bottom)
(Note that [0, 0] is also the case when snake runs out of the 480x480 board)
[food_dir_x, food_dir_y] gives the direction of food to snake head. It has 9 states:
food_dir_x: 0 (same coords on x axis), 1 (food on snake head left), 2 (food on snake head right)
food_dir_y: 0 (same coords on y axis), 1 (food on snake head top), 2 (food on snake head bottom)
[adjoining_body_top, adjoining_body_bottom, adjoining_body_left, adjoining_body_right] checks if there
is snake body in adjoining square of snake head. It has 8 states:
adjoining_body_top: 1 (adjoining top square has snake body), 0 (otherwise)
adjoining_body_bottom: 1 (adjoining bottom square has snake body), 0 (otherwise)
adjoining_body_left: 1 (adjoining left square has snake body), 0 (otherwise)
adjoining_body_right: 1 (adjoining right square has snake body), 0 (otherwise)
Actions: Your agent's actions are chosen from the set {up, down, left, right}.
Rewards: +1 when your action results in getting the food (snake head position is the same as the food position), -1
when the snake dies, that is when snake head hits the wall, its body segment or the head tries to move towards its
adjacent body segment (moving backwards). -0.1 otherwise (does not die nor get food).
Q-learning Agent
TrainedAgent
In this part of the assignment, you will create a snake agent to learn how to get food as many as possible without dying. In
order to do this, you must use Q-learning. Implement the TD Q-learning algorithm and train it on the MDP outlined above.
Q(s, a) ← Q(s, a) + α(R(s) + γ max Q(s′
, a′
) − Q(s, a)) a′
Also, use the exploration policy mentioned in class and use 1 for R+:
During training, your agent needs to update your Q-table first (this step is skipped when the initial state and action are
None), get the next action using the above exploration policy, and then update N-table with that action. If the game is over,
that is when the dead varaible becomes true, you only need to update your Q table and reset the game. During testing, your
agent only needs to give the best action using Q-table. Train it for as long as you deem necessary, counting the average
number of points your agent can get. Your average over 1000 test games should be at least 20. For grading purposes,
please submit code with the above exploration policy, state configurations and reward model. We will initialize your
agent class with different parameters (Ne, C, gamma), initialize environment with different initial snake and food
postion and compare the Q-table result at the point when the first food is eaten during training(see snake_main.py
for Q-table generation detail).
Once you have this working, you will need to adjust the learning rate, α (how about a fixed learning rate or other C value?),
the discount factor, γ, and the settings that you use to trade off exploration vs. exploitation.
In your report, please include the values of α, γ, and any parameters for your exploration settings that you used, and discuss
how you obtained these values. What changes happen in the game when you adjust any of these variables? How many
games does your agent need to simulate before it learns an optimal policy? After your Q-learning seems to have converged
to a good policy, run your algorithm on a large number of test games (≥1000) and report the average number of points.
In addition to discussing these things, try adjusting the state configurations that were defined above. If you think it would be
beneficial, you may also change the reward model to provide more informative feedback to the agent. Try to find
modifications that allow the agent to learn a better policy than the one you found before. In your report, describe the changes
you made and the new number of points the agent was able to get. What effect did this have on the time it takes to train your
agent? Include any other interesting observations.
Tips
Initially, all the Q value estimates should be 0.
The learning rate should decay as C/(C+N(s,a)), where N(s,a) is the number of times you have seen the given the
state-action pair.
When adjusting state configurations, try to make state numbers as small as possible to make the training easier. If the
state numbers are too large. The snake may stuck in an infinite loop.
In a reasonable implementation, you should see your average points increase in seconds.
You can run python snake_main.py --human to play the game yourself.
Debug Convenience
For debug convenience, we provide three debug examples of Q-table for you. Each Q-table is generated exactly after
snake eats the first food in training process. More specifically, it's the first time when snake reaches exactly 1 point in
training, see how the Q-table is generated and saved during training in snake_main.py. For example, you can run diff
checkpoint.npy checkpoint1.npy to see whether there is a difference. The only difference of these three debug examples is
the setting of parameters(initialized position of snake head and food, Ne, C and gamma).
Notice that for passing the autograder, if the scores of actions from exploration function are equal, the priority
should be right > left > down > up.
[Debug Example 1] snake_head_x=200, snake_head_y=200, food_x=80, food_y=80, Ne=40, C=40, gamma=0.7
checkpoint1.npy
[Debug Example 2] snake_head_x=200, snake_head_y=200, food_x=80, food_y=80, Ne=20, C=60, gamma=0.5
checkpoint2.npy
[Debug Example 3] snake_head_x=80, snake_head_y=80, food_x=200, food_y=200, Ne=40, C=40, gamma=0.7
checkpoint3.npy
Note that for one part of the autograder, we will run your training process on different settings of parameters and
compare the Q-table generated exactly when snake reaches 1 point first time in training, with ours. So making sure
you can pass these debug examples will help you a lot for passing this part of the autograder.
In addition, for the other part of autograder, we will test your performance using your q_agent.npy and agent.py.
Average points over 20 points on 1000 test games should be able to obatin full credit for this part.
Part 2: Deep Learning (MNIST Fashion)
Created by Austin Bae (Modified from Ryley Higa)
Background
By now you should be familiar with the MNIST dataset from MP3. In MP3, we trained a model on this dataset using Linear
Classifiers such as Naive Bayes and Perceptron. This time around, we will implement a fully connected neural network from
scratch on the same dataset we used in MP3.
Your task is to build a 4-layer neural network with 256 hidden nodes per layer except the last layer which should have 10
(number of classes) layers. You are going to use a method called Minibatch Gradient Descent, which runs for a given
number of iterations (epochs) and does the following per epoch:
1. Shuffle the training data
2. Split the data into batches (use batch size of 200)
3. For each batch (subset of data):
feed batch into the 4-layer neural network
Compute loss and update weights
4. Observe the total loss and go to next iteration
The Neural Network
Neural Network Architecture
One Layer Neural Network
For this assignment, you will build a 4-layer, fully connected Neural Network. You can think about a fully-connected Neural
Network as a series of multiple interconnected layers, in which are basically a group of nodes (See the diagram above). You
can think of each layer as the perceptron model you implemented in MP3, but instead of one perceptron, you have multiple
perceptrons that feed the output as the input to another.
Neural Network Layer
This is what each layer in a Neural Network looks like. Note that there are two directions: forward and backward
propagation. Forward Propagation uses both the inputs (Ain) and the weights (W, b) specific to that layer to compute the
output (Aout) to the next layer. Backward Propagation computes the gradient (derivative) of the loss function with respect
to the weights at each layer.
Inside of each propagation, there are two functions that both compute forward and backward. In general, Affine
transformations compute the affine output (Z) by doing some sort of computation with the input and weights (like matrix
multiply). Nonlinear Activation functions then take that affine output and transform it. There are numerous activation
functions out there, but for our assignment we will be using ReLU (Rectified Linear Units) which is just simply R(x) =
max(0, x). The backwards functions will compute the gradients of the inputs with respect to loss.
Then, at the last layer, the output should be the classification computed by your Neural Network. You have to then calculate
the loss, which would represent how well your network did in classifying correctly. The model's job is to minimize this loss,
since the better it does, the lower loss it will have. Again, we can use many different types of loss functions, but we will use
Cross-entropy for this assignment.
Implementation Details
You are to implement these 8 different functions inside of neural_network.py. You will only have to edit and submit this
file. Do not import any non-standard Python libraries other than numpy, or you will result in getting a 0 on the
autograder.
affine_forward(A, W, b)
Inputs: A (data with size n,d), W (weights with size d,d'), b (bias with size d')
Outputs: Z (affine output with size n,d'), cache (tuple of the original inputs)
affine_backward(dZ, cache)
Inputs: dZ (gradient of Z), cache (of the forward operation)
Outputs: dA, dW, db (gradients with respect to loss)
relu_forward(Z)
Inputs: Z (affine output with size n,d')
R(Z) = max(0,Z) : basically sets all negative values in matrix to 0
Outputs: A (Relu Output with size n,d'), cache object (Z)
relu_backward(dA, cache)
Inputs: dA (gradient of A), cache
dZij = 0 if Zij = 0. else dZij = dAij. Basically if Z was zeroed out at a point, then dZ should also be zeroed out
since it shouldn't contribute to the gradient at that location.
Outputs: dZ (gradient of Z)
cross_entropy(F, y)
Inputs: F (logits with size n, num_classes), y (actual class label of data with size n)
Fik refers to the score of classifying row i as class k. Fiyi refers to the score of F classifying row i as the actual
class given by yi
. So if the actual label for row i was 7, then Fiyi = Fi7. The 1{j = yi} function in the gradient
calculation is just a binary function that outputs either a 0 or a 1 if the condition is met.
Output: loss, dF (gradient of the logits)
four_nn() - 4 layer neural network function
This function's inputs and outputs are up to you, and it won't be autograded
The Neural Network must have 4 layers, with (256, 256, 256, and num_classes) nodes per layers
In this function, you should use all of your helper functions above
This should be called inside of both minibatch_gd() and test_nn()
Here is the pseudocode for a 3 layer neural network, as reference.
You should use a learning rate (eta) of 0.1
minibatch_gd(epoch, w1, w2, w3, w4, b1, b2, b3, b4, x_train, y_train, num_classes, shuffle=True)
This function will implement your minibatch gradient descent (model training).
Use batch size of 200. You can assume that len(x_train) will always be divisible by 200.
Inputs:
epoch: number of iterations
w1, w2, w3, w4: Your weights corresponding to each of the layers
b1, b2, b3, b3: Your biases corresponding to each of the layers
x_train, y_train: Numpy arrays of the features and labels
num_classes: number of classes. This should be 10 for our dataset. Reason we pass this as a parameter is
that your model should be able to run for datasets of any size.
shuffle: boolean flag indicating whether you should shuffle your data at each epoch. By default this is set
to True. This will be set to false during testing/autograding, so adjust your code so that it will shuffle only
if this boolean flag is set to True.
Here is a pseudocode for reference:
Outputs:
all the modified weights, biases (w1 ~ w4, b1 ~ b4)
losses: a list of total loss at each epoch. Note that this will be a list with length = epoch
test_nn(w1, w2, w3, w4, b1, b2, b3, b4, x_test, y_test, num_classes)
This function will evaluate how well your trained model performs in classifying test data.
Inputs:
w1 ~ w4, b1 ~ b4: trained weights/biases at each layer
x_test, y_test: Numpy arrays of the test features and labels
num_classes: Number of classes (10)
Outputs: Average Classification Rate, Average Classification Rate per Class (List with size=num_classes)
Testing
We have provided you with a unit testing script nn_test.py that will check if your individual functions produce the right
output. Passing these unit tests are a good but incomplete measure of whether or not your functions are correct. This test
script also checks if your minibatch gradient descent works for a smaller dataset, so you should confirm that you pass these
tests before running nn_main.py on the actual MNIST data. Feel free to modify this file as you need, this file won't be
submitted nor graded.
What to Report
Run your minibatch gradient for 10, 30, and 50 epochs. You should start with clean generated weights/biases for each run.
For each run, report:
1. Confusion Matrix
2. Average Classification Rate
3. Runtime of your minibatch gradient function
At the end of 50 epochs, include a graph that plots epochs vs losses.
Also report any interesting observations, and possible explanations for those observations.
Extra Credit Opportunity
Convolutional Neural Network (CNN) is a different type of Neural Network that is very popular, especially in image
processing. Implement a CNN using the same MNIST fashion dataset using any deep learning frameworks of your choice
(Tensorflow, Pytorch, Keras, etc). You may reference online materials, but make sure to cite them as necessary!
Write a paragraph on how CNNs work, what you implemented, and the results of your CNN. Compare it to theperformance
of your fully-connected 4 layer network. Report the confusion matrix, average classification rate, and average classification
rate per class of the test data once your model converges. The extra credit will be capped to 10% of the entire MP grade.
Submit your file as nn_extracredit.ext, ext being the extension of the file. You do not have to submit any runtime
information (modules/metadata) but make sure to describe your algorithm in the report.
Warning/Hints
One disadvantage of Neural Networks is that they may take a long time to compute. Given that fact, correctness of your
algorithms may not be sufficient to successfully complete this assignment. Use numpy functions (matrix multiply,
broadcasting) as much as possible as opposed to native python loops, as this will significantly decrease your runtime.
With decently optimized code, we were able to get under 10 seconds per epoch on a 2015 Macbook Pro, and roughly under
80 seconds on EWS. We highly recommend you running the code on a machine faster than EWS, but EWS should give you
the highest-bound on computation time. We will cut off computation for autograding at around 120 seconds per epoch on
EWS. Also, running your model for 50 epochs will take somewhere around 10 mins ~ 1 hour. If you do this last minute you
may not be able to run your computations on time. There will be no excuses for missing the deadline.
We will also grade your code on a different dataset that will have different number of rows (but divisible by 200), features,
and target classes. So it is very important that you don't hardcode any numbers into your algorithms, and it should be able to
support data of any size. Provided hyperparameters (number of layers, number of nodes per layer, learning rate, batch size)
should remain the same.
Provided Code Skeleton
We have provided skeleton.zip with the descriptions below. For Part 1, do not import any non-standard libraries except
pygame (pygame version 1.9.4) and numpy. For Part 2, do not import any non-standard library except numpy. Failure
to do so will result in a 0 in the autograder.
Part 1
snake.py - This is the file that defines the snake environment and creates the GUI for the game.
utils.py - This is the file that defines some of the discretization constants as defined above and contains the functions
to save and load models.
agent.py This is the file where you will be doing all of your work. This file contains the Agent class. This is the agent
you will implement to act in the snake environment. Below is the list of instance variables and functions in the Agent
class.
self._train: This is a boolean flag variable that you should use to determine if the agent is in train or test mode.
In train mode, the agent should explore(based on exploration function) and exploit based on the Q table. In test
mode, the agent should purely exploit and always take the best action.
train(): This function sets the self._train to be True. This is called before the training loop is run in
snake_main.py
test(): This function sets the self._train to be False. This is called before the testing loop is run in
snake_main.py
save_model(): This function saves the self.Q table. This is called after the training loop in snake_main.py.
load_model(): This function loads the self.Q table. This is called before the testing loop in snake_main.py.
act(state, points, dead): This is the main function you will implement and is called repeatedly by
snake_main.py while games are being run. "state" is the state from the snake environment and is a list of
[snake_head_x, snake_head_y, snake_body, food_x, food_y](Notice that in act
function, you first need to discretize this into the state configuration we
defined above). "points" is the number of food the snake has eaten. "dead" is a
boolean indicating if the snake is dead. "points", "dead" should be used to define
your reward function. act should return a number from the set of {0,1,2,3}.
Returning 0 will move the snake agent up, returning 1 will move the snake agent
down, and returning 2 will move the agent left, returning 3 will move the agent
right. If self._train is True, this function should update the Q table and return an
action(Notice that if the scores of actions from exploration function are equal,
the priority should be right > left > down > up). If self._train is False, the agent
should simply return the best action based on the Q table.
snake_main.py - This is the main file that starts the program. This file runs the snake
game with your implemented agent acting in it. The code runs a number of training
games, then a number of testing games, and then displays example games at the end.
Do not modify the provided code. You will only have to modify agent.py
Part 2
neural_network.py - The only file that you will have to modify. Descriptions of the
functions are in the descriptions above.
nn_test.py - Unit test module for you to use. Simply
run it using python nn_test.py. nn_main.py - Main
function that will run your code written in
nueral_network.py. data - folder that contains the train
and test numpy files, exactly the same from MP3
tests - You don't have to touch this since all the text parsing has been done for you in nn_test.py
You can modify the test/main functions as long as your inputs/outputs for all functions inside of
neural_network.py is consistent with the instructions. Note that only neural_network.py will be
submitted & graded.
Deliverables
Please submit only the following files.
agent.py with the same exploration policy, state configurations and reward model mentioned above.
q_agent.npy the best numpy array trained by you with the same state configurations
mentioned above. (Can be saved by passing "--model_name q_agent.npy" to
snake_main.py). Note that this model above should work without modifying any
code files other than agent.py.
neural_network.py with all the functions for part 2.
report.pdf
Report Checklist
Part 1
1. Briefly describe the
implementation of
your agent snake.
How does the agent
act during train
phase?
How does the agent act during test phase?
2. Use Ne, C (or fixed alpha?), gamma that you believe to be the best. After training has
converged, run your algorithm on 1000 test games and report the average point.
Give the value of Ne, C (or fixed
alpha) you believe to be the best.
Report the training convergence time.
Report average point on 1000 test games.
3. Describe the changes you made to your MDP(state configuration, exploration policy and
reward model), at least make changes to state configuration. Report the performance
(the average points on 1000 test games). Notice that training your modified state space
should give you at least 10 points in average for 1000 test games. Explain why these
changes are reasonable, observe how snake acts after changes and analyze the positive
and negative effects they have. Notice again, make sure your submitted agent.py and
q_agent.npy are without these changes and your changed MDP should not be
submitted.
Part 2
1. Briefly describe any optimizations you performed on your functions for faster runtime.
2. Report Confusion Matrix, Average Classification Rate, and Runtime of Minibatch
gradient for 10, 30, 50 epochs. This means that you should have 3x3=9 total items in this
section.
3. Add a graph that plots epochs vs losses at that epoch. (For 50 epochs)
4. Describe any trends that you see. Is this expected or surprising? What do you
think is the explanation for the observations?
5. Report Extra Credit section, if any.