Untrained vs Trained

For each task I have added a command line API, so you can choose to run any model in either TRAIN or TEST mode. You can also select to run a model with specific configurations. In test mode code will pick models automatically based on arguments passed. For each task following are the API avaliable to execute the code from command line

Dependencies

• Python 3.5.2
• TensorFlow version 1.0
• gym
• sklearn
• numpy
• pandas
• matplotlib
• scikit-image
• random

Problem A

1 (GPU trained)

Code: code/problemA1.py

API to run the code from command line:

Command: python problemA1.py

Example: python problemA1.py

---

2 (GPU trained)

Code: code/problemA2.py

API to run the code from command line:

Command: python problemA2.py

Example: python problemA2.py

---

3 (GPU trained)

Code: code/problemA3.py

Model folders: (for all learning rates, models gets restores automatically based on arguments passed in commands)
Linear

1. models/problemA3/linear/1e-05
2. models/problemA3/linear/0.00001
3. models/problemA3/linear/0.0001
4. models/problemA3/linear/0.001
5. models/problemA3/linear/0.01
6. models/problemA3/linear/0.5

Hidden layer

1. models/problemA3/hidden/1e-05
2. models/problemA3/hidden/0.00001
3. models/problemA3/hidden/0.0001
4. models/problemA3/hidden/0.001
5. models/problemA3/hidden/0.01
6. models/problemA3/hidden/0.5

API to run the code from command line:

Command: python problemA3.py <arg1> <arg2> <arg3> <arg4>
Where,
arg1 = (str) mode: e.g. train, test
arg2 = (str) type: e.g. linear, hidden
arg3 = (int) batch size: e.g. 32 (only for train mode)
arg4 = (int) epochs: e.g. 50 or 100 (only for train mode)

Example: python problemA3.py train linear 32 50
python problemA3.py test linear

---

4 (GPU trained)

Code: code/problemA4.py

Model folders: (models gets restores automatically based on arguments passed in commands)

1. models/problemA4/

API to run the code from command line:

Command: python problemA4.py <arg1> <arg2>
Where,
arg1 = (str) mode: e.g. train, test
arg2 = (float) learning rate: e.g. 0.0001

Example: python problemA4.py test 32 50

---

5 (GPU trained)

Code: code/problemA5.py

Model folders: (models gets restores automatically based on arguments passed in commands)

1. models/problemA5/1000
2. models/problemA5/30

API to run the code from command line:

Command: python problemA5.py <arg1> <arg2> <arg3>
Where,
arg1 = (str) mode: e.g. train, test
arg2 = (int) hidden layer e.g. 30 or 1000
arg3 = (float) learning rate: e.g. 0.0001

Example: python problemA5.py test 1000 0.0001

---

6 (GPU trained)

Code: code/problemA6.py

Model folders: (models gets restores automatically based on arguments passed in commands)

1. models/problemA6

API to run the code from command line:

Command: python problemA6.py <arg1> <arg2> <arg3>
Where,
arg1 = (str) mode: e.g. train, test
arg2 = (int) batch size: e.g. 32 (only for train mode)
arg3 = (float) learning rate: e.g. 0.0001

Example: python problemA6.py test 32 0.0001

---

7 (GPU trained)

Code: code/problemA7.py

Model folders: (models gets restores automatically based on arguments passed in commands)

1. models/problemA7

API to run the code from command line:

Command: python problemA7.py <arg1> <arg2> <arg3>
Where,
arg1 = (str) mode: e.g. train, test
arg2 = (int) batch size: e.g. 32 (only for train mode)
arg3 = (float) learning rate: e.g. 0.0001

Example: python problemA7.py test 32 0.0001

---

8 (GPU trained)

Code: code/problemA8.py

Model folders: (models gets restores automatically based on arguments passed in commands)

1. models/problemA8

API to run the code from command line:

Command: python problemA8.py <arg1> <arg2> <arg3>
Where,
arg1 = (str) mode: e.g. train, test
arg2 = (int) batch size: e.g. 32 (only for train mode)
arg3 = (float) learning rate: e.g. 0.0001

Example: python problemA8.py test 32 0.0001

Problem B (CPU trained)

Code: code/problemB-targetnet.py

Models folders: (Trained with GRU only)

1. Pong-v3: models/Pong-v3
2. Boxing-v3: models/Boxing-v3
3. MsPacman-v3: models/MsPacman-v3

API to run the code from command line:

Command: python problemB-targetnet.py <arg1> <arg2> <arg3> <arg4> <arg5>
Where,
arg1 = (str) agent: e.g. pong, boxing or pacman
arg1 = (int) question: e.g. 1, 2 or 3
arg3 = (str) mode: e.g. train, test
arg4 = (int) batch size: e.g. 32 (only for train mode)
arg5 = (float) learning rate: e.g. 0.0001

Example: python problemB-targetnet.py pong 3 test 32 0.0001

---

Setup to run source code

1. Install TensorFlow on Anaconda environment (gpu version prefered for speed of execution), setup for windows
2. Install numpy, sklearn, matplotlib if not installed by default.
3. Activate tensforflow environment. e.g. activate tensorflow-gpu
4. Navigate to source code directory and run each python file. (command line API prefered)

P.S. The saved parameters have been trained using tensorflow version APIr1.0